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793 Einträge « ‹ 1 von 16 › » 
2024
793.
 Büttner, Sabine;  Handmann, Uwe;  Irrek, Wolfgang
Transformation zur Circular Economy - Kleine und mittlere Unternehmen im Wandel begleiten Buch 
Springer, 2024, (in print).
Links | BibTeX | Schlagwörter: 
@book{CE-IrrHanBue2024,

title = {Transformation zur Circular Economy - Kleine und mittlere Unternehmen im Wandel begleiten},

author = {Sabine Büttner and Uwe Handmann and Wolfgang Irrek},

editor = {Sabine Büttner and Uwe Handmann and Wolfgang Irrek},

url = {https://link.springer.com/book/9783658433376},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

publisher = {Springer},

note = {in print},

keywords = {},

pubstate = {published},

tppubtype = {book}

}

Schließen
https://link.springer.com/book/9783658433376
Schließen
2023
792.
 Ali, Omair;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis;  Klaes, Christian
ConTraNet: A Hybrid Network for Improving the Classification of EEG and EMG Signals with Limited Training Data Artikel 
In: Computers in Biology and Medicine, S. 107649, 2023, ISSN: 0010-4825.
Abstract | Links | BibTeX | Schlagwörter: BCI, Brain computer interface, Deep learning, EEG decoding, EMG decoding, Machine Learning
@article{aliConTraNetHybridNetwork2023,

title = {ConTraNet: A Hybrid Network for Improving the Classification of EEG and EMG Signals with Limited Training Data},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

url = {https://www.sciencedirect.com/science/article/pii/S0010482523011149},

doi = {10.1016/j.compbiomed.2023.107649},

issn = {0010-4825},

year  = {2023},

date = {2023-11-02},

urldate = {2023-11-02},

journal = {Computers in Biology and Medicine},

pages = {107649},

abstract = {Objective Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. Approach In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. Main results We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). Significance With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.},

keywords = {BCI, Brain computer interface, Deep learning, EEG decoding, EMG decoding, Machine Learning},

pubstate = {published},

tppubtype = {article}

}

Schließen
Objective Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. Approach In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. Main results We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). Significance With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.
Schließen
https://www.sciencedirect.com/science/article/pii/S0010482523011149
doi:10.1016/j.compbiomed.2023.107649
Schließen
791.
 Sziburis, Tim;  Blex, Susanne;  Iossifidis, Ioannis
Variability Study of Human Hand Motion during 3D Center-out Tasks Captured for the Diagnosis of Movement Disorders Proceedings Article 
In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023.
Abstract | BibTeX | Schlagwörter: movement model
@inproceedings{sziburisVariabilityStudyHuman2023,

title = {Variability Study of Human Hand Motion during 3D Center-out Tasks Captured for the Diagnosis of Movement Disorders},

author = {Tim Sziburis and Susanne Blex and Ioannis Iossifidis},

year  = {2023},

date = {2023-09-15},

urldate = {2023-09-15},

booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

abstract = {Variability analysis bears the potential to differentiate between healthy and pathological human movements [1]. Our study is conducted in the context of developing a portable glove for the diagnosis of movement disorders. This proposal has methodical as well as technical requirements. Generally, the identification of movement disorders via an analysis of motion data needs to be confirmed within the given setup. Typically, rhythmic movements like gait or posture control are examined for their variability, but here, the characteristic pathological traits of arm movement like tremors are under observation. In addition, the usability of a portable sensor instead of a stationary tracking system has to be validated. In this part of the project, human motion data are recorded redundantly by both an optical tracking system and an IMU. In our setup, a small cylinder is transported in three-dimensional space from a unified start position to one of nine target positions, which are equidistantly aligned on a semicircle. 10 trials are performed per target and hand, resulting in 180 trials per participant in total. 31 participants (11 female and 20 male) without known movement disorders, aged between 21 and 78 years, took part in the study. In addition, the 10-item EHI is used. The purpose of the analysis is to compare different variability measures to uncover differences between trials (intra-subject variability) and participants (inter-subject variability), especially in terms of age and handedness effects. Particularly, a novel variability measure is introduced which makes use of the characteristic planarity of the examined hand paths [2]. For this, the angle of the plane which best fits the travel phase of the trajectory is determined. In addition to neurological motivation, the advantage of this measure is that it allows the comparison of trials of different time spans and to different target directions without depending on trajectory warping. In the future, measurements of the same experimental setup with patients experiencing movement disorders are planned. For the subsequent pathological analysis, this study provides a basis in terms of methodological considerations and ground truth data of healthy participants. In parallel, the captured motion data are modelled utilizing dynamical systems (extended attractor dynamics approach). For this approach, the recorded and modelled data can be compared by the variability measures examined in this study.},

keywords = {movement model},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Variability analysis bears the potential to differentiate between healthy and pathological human movements [1]. Our study is conducted in the context of developing a portable glove for the diagnosis of movement disorders. This proposal has methodical as well as technical requirements. Generally, the identification of movement disorders via an analysis of motion data needs to be confirmed within the given setup. Typically, rhythmic movements like gait or posture control are examined for their variability, but here, the characteristic pathological traits of arm movement like tremors are under observation. In addition, the usability of a portable sensor instead of a stationary tracking system has to be validated. In this part of the project, human motion data are recorded redundantly by both an optical tracking system and an IMU. In our setup, a small cylinder is transported in three-dimensional space from a unified start position to one of nine target positions, which are equidistantly aligned on a semicircle. 10 trials are performed per target and hand, resulting in 180 trials per participant in total. 31 participants (11 female and 20 male) without known movement disorders, aged between 21 and 78 years, took part in the study. In addition, the 10-item EHI is used. The purpose of the analysis is to compare different variability measures to uncover differences between trials (intra-subject variability) and participants (inter-subject variability), especially in terms of age and handedness effects. Particularly, a novel variability measure is introduced which makes use of the characteristic planarity of the examined hand paths [2]. For this, the angle of the plane which best fits the travel phase of the trajectory is determined. In addition to neurological motivation, the advantage of this measure is that it allows the comparison of trials of different time spans and to different target directions without depending on trajectory warping. In the future, measurements of the same experimental setup with patients experiencing movement disorders are planned. For the subsequent pathological analysis, this study provides a basis in terms of methodological considerations and ground truth data of healthy participants. In parallel, the captured motion data are modelled utilizing dynamical systems (extended attractor dynamics approach). For this approach, the recorded and modelled data can be compared by the variability measures examined in this study.
Schließen
790.
 Fidencio, Aline Xavier;  Klaes, Christian;  Iossifidis, Ioannis
Exploring Error-related Potentials in Adaptive Brain-Machine Interfaces: Challenges and Investigation of Occurrence and Detection Ratios Proceedings Article 
In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023.
Abstract | BibTeX | Schlagwörter: BCI, EEG, Machine Learning
@inproceedings{xavierfidencioExploringErrorrelatedPotentials2023,

title = {Exploring Error-related Potentials in Adaptive Brain-Machine Interfaces: Challenges and Investigation of Occurrence and Detection Ratios},

author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},

year  = {2023},

date = {2023-09-15},

urldate = {2023-09-15},

booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

abstract = {Non-invasive techniques like EEG can record error-related potentials (ErrPs), neural signals associated with error processing and awareness. ErrPs are generated in response to self-made and external errors, including those produced by the BMI. Since ErrPs are implicitly elicited and don’t add extra workload for the subject, they serve as a natural and intrinsic feedback source for developing adaptive BMIs. In our study, we assess the occurrence of interaction ErrPs in an adaptive BMI that combines ErrPs and reinforcement learning. We intentionally provoke ErrPs when the BMI misinterprets the user’s intention and performs an incorrect action. Subjects participated in a game controlled by a keyboard and/or motor imagery (imagining hand movements), and EEG data were recorded using an eight-electrode gel-based EEG system. Results reveal that obtaining a distinct ErrPs signal for each subject is more challenging than anticipated. Current practices report the ErrP in terms of over all subjects and trials difference grand average (error minus correct). This approach has, however, the limitation of masking the inter-trial and subject variability, which are relevant for the online single-trial detection of such signals. Moreover, the reported ErrPs waveshape exhibit differences in terms of components observed, as well as their respective latencies, even when very similar tasks are used. Consequently, we conducted additional individualized data analysis to gain deeper insights into the single-trial nature of the ErrPs. As a result, we determined the need for a better understanding and further investigation of how effectively the ErrPs waveforms generalize across subjects, tasks, experimental protocols, and feedback modalities. Given the challenges in obtaining a clear signal for all subjects and the limitations found in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether an error signal measurable at the scalp level is consistently generated when subjects encounter erroneous conditions. To address this question, we will assess the occurrence-to-detection ratio of ErrPs using invasive and non-invasive recording techniques, examining how uncertainties regarding error generation in the brain impact the learning pipeline.},

keywords = {BCI, EEG, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Non-invasive techniques like EEG can record error-related potentials (ErrPs), neural signals associated with error processing and awareness. ErrPs are generated in response to self-made and external errors, including those produced by the BMI. Since ErrPs are implicitly elicited and don’t add extra workload for the subject, they serve as a natural and intrinsic feedback source for developing adaptive BMIs. In our study, we assess the occurrence of interaction ErrPs in an adaptive BMI that combines ErrPs and reinforcement learning. We intentionally provoke ErrPs when the BMI misinterprets the user’s intention and performs an incorrect action. Subjects participated in a game controlled by a keyboard and/or motor imagery (imagining hand movements), and EEG data were recorded using an eight-electrode gel-based EEG system. Results reveal that obtaining a distinct ErrPs signal for each subject is more challenging than anticipated. Current practices report the ErrP in terms of over all subjects and trials difference grand average (error minus correct). This approach has, however, the limitation of masking the inter-trial and subject variability, which are relevant for the online single-trial detection of such signals. Moreover, the reported ErrPs waveshape exhibit differences in terms of components observed, as well as their respective latencies, even when very similar tasks are used. Consequently, we conducted additional individualized data analysis to gain deeper insights into the single-trial nature of the ErrPs. As a result, we determined the need for a better understanding and further investigation of how effectively the ErrPs waveforms generalize across subjects, tasks, experimental protocols, and feedback modalities. Given the challenges in obtaining a clear signal for all subjects and the limitations found in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether an error signal measurable at the scalp level is consistently generated when subjects encounter erroneous conditions. To address this question, we will assess the occurrence-to-detection ratio of ErrPs using invasive and non-invasive recording techniques, examining how uncertainties regarding error generation in the brain impact the learning pipeline.
Schließen
789.
 Grün, Felix;  Iossifidis, Ioannis
Investigation of the Interplay of Model-Based and Model-Free Learning Using Reinforcement Learning Proceedings Article 
In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023.
Abstract | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning
@inproceedings{grunInvestigationInterplayModelBased2023,

title = {Investigation of the Interplay of Model-Based and Model-Free Learning Using Reinforcement Learning},

author = {Felix Grün and Ioannis Iossifidis},

year  = {2023},

date = {2023-09-15},

urldate = {2023-09-15},

booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

abstract = {The reward prediction error hypothesis of dopamine in the brain states that activity of dopaminergic neurons in certain brain regions correlates with the reward prediction error that corresponds to the temporal difference error, often used as a learning signal in model free reinforcement learning (RL). This suggests that some form of reinforcement learning is used in animal and human brains when learning a task. On the other hand, it is clear that humans are capable of building an internal model of a task, or environment, and using it for planning, especially in sequential tasks. In RL, these two learning approaches, model-driven and reward-driven, are known as model based and model-free RL approaches. Both systems were previously thought to exist in parallel, with some higher process choosing which to use. A decade ago, research suggested both could be used concurrently, with some subject-specific weight assigned to each [1]. Still, the prevalent belief appeared to be that model-free learning is the default mechanism used, replaced or assisted by model-based planning only when the task demands it, i.e. higher rewards justify the additional cognitive effort. Recently, Feher da Silva et al. [2] questioned this belief, presenting data and analyses that indicate model-based learning may be used on its own and can even be computationally more efficient. We take a RL perspective, consider different ways to combine model-based and model-free approaches for modeling and for performance, and discuss how to further study this interplay in human behavioral experiments.},

keywords = {Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
The reward prediction error hypothesis of dopamine in the brain states that activity of dopaminergic neurons in certain brain regions correlates with the reward prediction error that corresponds to the temporal difference error, often used as a learning signal in model free reinforcement learning (RL). This suggests that some form of reinforcement learning is used in animal and human brains when learning a task. On the other hand, it is clear that humans are capable of building an internal model of a task, or environment, and using it for planning, especially in sequential tasks. In RL, these two learning approaches, model-driven and reward-driven, are known as model based and model-free RL approaches. Both systems were previously thought to exist in parallel, with some higher process choosing which to use. A decade ago, research suggested both could be used concurrently, with some subject-specific weight assigned to each [1]. Still, the prevalent belief appeared to be that model-free learning is the default mechanism used, replaced or assisted by model-based planning only when the task demands it, i.e. higher rewards justify the additional cognitive effort. Recently, Feher da Silva et al. [2] questioned this belief, presenting data and analyses that indicate model-based learning may be used on its own and can even be computationally more efficient. We take a RL perspective, consider different ways to combine model-based and model-free approaches for modeling and for performance, and discuss how to further study this interplay in human behavioral experiments.
Schließen
788.
 Schmidt, Marie Dominique;  Iossifidis, Ioannis
The Link between Muscle Activity and Upper Limb Kinematics Proceedings Article 
In: BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2023.
Abstract | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{schmidtLinkMuscleActivity2023,

title = {The Link between Muscle Activity and Upper Limb Kinematics},

author = {Marie Dominique Schmidt and Ioannis Iossifidis},

year  = {2023},

date = {2023-09-15},

urldate = {2023-09-15},

booktitle = {BC23 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

abstract = {The upper limbs are crucial in performing daily tasks that require strength, a wide range of motion, and precision. To achieve coordinated motion, planning and timing are critical. Sensory information about the target and the current body state is essential, as well as integrating past experiences, represented by pre-learned inverse dynamics that generate associated muscle activity. We propose a generative model that predicts upper limb muscle activity from a variety of simple and complex everyday motions by means of a recurrent neural network. The model shows promising results, with a good fit for different subjects and abstracts well for new motions. We handle the high inter-subject variation in muscle activity using a transfer learning approach, resulting in a good fit for new subjects. Our approach has implications for fundamental movement control understanding and the rehabilitation of neuromuscular diseases using myoelectric prostheses and functional electrical stimulation. Our model can efficiently predict both muscle activity and motion trajectory, which can assist in developing more effective rehabilitation techniques.},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
The upper limbs are crucial in performing daily tasks that require strength, a wide range of motion, and precision. To achieve coordinated motion, planning and timing are critical. Sensory information about the target and the current body state is essential, as well as integrating past experiences, represented by pre-learned inverse dynamics that generate associated muscle activity. We propose a generative model that predicts upper limb muscle activity from a variety of simple and complex everyday motions by means of a recurrent neural network. The model shows promising results, with a good fit for different subjects and abstracts well for new motions. We handle the high inter-subject variation in muscle activity using a transfer learning approach, resulting in a good fit for new subjects. Our approach has implications for fundamental movement control understanding and the rehabilitation of neuromuscular diseases using myoelectric prostheses and functional electrical stimulation. Our model can efficiently predict both muscle activity and motion trajectory, which can assist in developing more effective rehabilitation techniques.
Schließen
787.
 Hussain, Muhammad Ayaz;  Iossifidis, Ioannis
Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller Artikel 
In: arXiv:2309.04698 [cs.RO], 2023.
Abstract | Links | BibTeX | Schlagwörter: Autonomous robotics, BCI, Computer Science - Artificial Intelligence, Computer Science - Information Theory, Computer Science - Machine Learning, Exoskeleton
@article{ayazhussainAdvancementsUpperBody2023,

title = {Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller},

author = {Muhammad Ayaz Hussain and Ioannis Iossifidis},

url = {https://doi.org/10.48550/arXiv.2309.04698},

doi = {10.48550/arXiv.2309.04698},

year  = {2023},

date = {2023-09-09},

urldate = {2023-09-09},

journal = {arXiv:2309.04698 [cs.RO]},

abstract = {In this study, we present a feedforward control system designed for active gravity compensation on an upper body exoskeleton. The system utilizes only positional data from internal motor sensors to calculate torque, employing analytical control equations based on Newton-Euler Inverse Dynamics. Compared to feedback control systems, the feedforward approach offers several advantages. It eliminates the need for external torque sensors, resulting in reduced hardware complexity and weight. Moreover, the feedforward control exhibits a more proactive response, leading to enhanced performance. The exoskeleton used in the experiments is lightweight and comprises 4 Degrees of Freedom, closely mimicking human upper body kinematics and three-dimensional range of motion. We conducted tests on both hardware and simulations of the exoskeleton, demonstrating stable performance. The system maintained its position over an extended period, exhibiting minimal friction and avoiding undesired slewing.},

keywords = {Autonomous robotics, BCI, Computer Science - Artificial Intelligence, Computer Science - Information Theory, Computer Science - Machine Learning, Exoskeleton},

pubstate = {published},

tppubtype = {article}

}

Schließen
In this study, we present a feedforward control system designed for active gravity compensation on an upper body exoskeleton. The system utilizes only positional data from internal motor sensors to calculate torque, employing analytical control equations based on Newton-Euler Inverse Dynamics. Compared to feedback control systems, the feedforward approach offers several advantages. It eliminates the need for external torque sensors, resulting in reduced hardware complexity and weight. Moreover, the feedforward control exhibits a more proactive response, leading to enhanced performance. The exoskeleton used in the experiments is lightweight and comprises 4 Degrees of Freedom, closely mimicking human upper body kinematics and three-dimensional range of motion. We conducted tests on both hardware and simulations of the exoskeleton, demonstrating stable performance. The system maintained its position over an extended period, exhibiting minimal friction and avoiding undesired slewing.
Schließen
https://doi.org/10.48550/arXiv.2309.04698
doi:10.48550/arXiv.2309.04698
Schließen
786.
 Schmidt, Marie D.;  Glasmachers, Tobias;  Iossifidis, Ioannis
The Concepts of Muscle Activity Generation Driven by Upper Limb Kinematics Artikel 
In: BioMedical Engineering OnLine, Bd. 22, Nr. 1, S. 63, 2023, ISSN: 1475-925X.
Abstract | Links | BibTeX | Schlagwörter: Artificial generated signal, BCI, Electromyography (EMG), Generative model, Inertial measurement unit (IMU), Machine Learning, Motion parameters, Muscle activity, Neural networks, transfer learning, Voluntary movement
@article{schmidtConceptsMuscleActivity2023,

title = {The Concepts of Muscle Activity Generation Driven by Upper Limb Kinematics},

author = {Marie D. Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

url = {https://doi.org/10.1186/s12938-023-01116-9},

doi = {10.1186/s12938-023-01116-9},

issn = {1475-925X},

year  = {2023},

date = {2023-06-24},

urldate = {2023-06-24},

journal = {BioMedical Engineering OnLine},

volume = {22},

number = {1},

pages = {63},

abstract = {The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.},

keywords = {Artificial generated signal, BCI, Electromyography (EMG), Generative model, Inertial measurement unit (IMU), Machine Learning, Motion parameters, Muscle activity, Neural networks, transfer learning, Voluntary movement},

pubstate = {published},

tppubtype = {article}

}

Schließen
The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.
Schließen
https://doi.org/10.1186/s12938-023-01116-9
doi:10.1186/s12938-023-01116-9
Schließen
785.
 Saif-ur-Rehman, Muhammad;  Ali, Omair;  Klaes, Christian;  Iossifidis, Ioannis
Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting Artikel 
In: arXiv:2304.01355 [cs, math, q-bio], 2023.
Links | BibTeX | Schlagwörter: BCI, Machine Learning, Spike Sorting
@article{saifurrehman2023adaptive,

title = {Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting},

author = {Muhammad Saif-ur-Rehman and Omair Ali and Christian Klaes and Ioannis Iossifidis},

doi = {10.48550/arXiv.2304.01355},

year  = {2023},

date = {2023-05-02},

urldate = {2023-05-02},

journal = {arXiv:2304.01355 [cs, math, q-bio]},

keywords = {BCI, Machine Learning, Spike Sorting},

pubstate = {published},

tppubtype = {article}

}

Schließen
doi:10.48550/arXiv.2304.01355
Schließen
784.
 Irrek, Wolfgang;  Handmann, Uwe
Sauber getrennt ist halb verwertet - Recycling mittels KI Artikel 
In: IM+io Best & Next Practices aus Digitalisierung, Management, Wissenschaft, Bd. 2023, Nr. 01, S. 26-29, 2023, ISSN: 1616-1017.
Links | BibTeX | Schlagwörter: 
@article{IMio-IrrHan2023,

title = {Sauber getrennt ist halb verwertet - Recycling mittels KI},

author = {Wolfgang Irrek and Uwe Handmann},

url = {https://www.im-io.de/wegeausdemmangel/sauber-getrennt-ist-halb-verwertet/},

issn = {1616-1017},

year  = {2023},

date = {2023-03-01},

urldate = {2023-03-01},

journal = {IM+io Best & Next Practices aus Digitalisierung, Management, Wissenschaft},

volume = {2023},

number = {01},

pages = {26-29},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Schließen
https://www.im-io.de/wegeausdemmangel/sauber-getrennt-ist-halb-verwertet/
Schließen
783.
 Rohrschneider, David;  Baker, Nermeen Abou;  Handmann, Uwe
Double Transfer Learning to detect Lithium-Ion batteries on X-Ray images Konferenz 
17th International Work-Conference on Artificial Neural Networks (IWANN 2023), Ponta Delgada, Portugal, June 19 - 21, 2023, Proceedings, Part I, Bd. 14134, Lecture Notes in Computer Science (LNCS) Springer Nature, Switzerland, 2023.
Links | BibTeX | Schlagwörter: 
@conference{IWANN-RohBakHan2023,

title = {Double Transfer Learning to detect Lithium-Ion batteries on X-Ray images},

author = {David Rohrschneider and Nermeen Abou Baker and Uwe Handmann},

url = {https://link.springer.com/chapter/10.1007/978-3-031-43085-5_14},

doi = {10.1007/978-3-031-43085-5_14},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {17th International Work-Conference on Artificial Neural Networks (IWANN 2023), Ponta Delgada, Portugal, June 19 - 21, 2023, Proceedings, Part I},

volume = {14134},

pages = {175-188},

address = {Springer Nature, Switzerland},

series = {Lecture Notes in Computer Science (LNCS)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Schließen
https://link.springer.com/chapter/10.1007/978-3-031-43085-5_14
doi:10.1007/978-3-031-43085-5_14
Schließen
782.
 Baker, Nermeen Abou;  Handmann, Uwe
Don't waste SAM Konferenz 
The 31th European Symposium on Artificial Neural Networks (ESANN 2023), Bruges, Belgium, 2023.
Links | BibTeX | Schlagwörter: 
@conference{ESANN-BakHan2023,

title = {Don't waste SAM},

author = {Nermeen Abou Baker and Uwe Handmann},

url = {https://www.esann.org/sites/default/files/proceedings/2023/ES2023-116.pdf},

doi = {10.14428/esann/2023.ES2023-116},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {The 31th European Symposium on Artificial Neural Networks (ESANN 2023)},

pages = {429-434},

address = {Bruges, Belgium},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Schließen
https://www.esann.org/sites/default/files/proceedings/2023/ES2023-116.pdf
doi:10.14428/esann/2023.ES2023-116
Schließen
781.
 Dia, Agostino Di;  Riebner, Tim;  Arntz, Alexander;  Jansen, Marc
Prototyping a Smart Contract Application for Fair Reward Distribution in Software Development Projects Proceedings Article 
In:  Prieto, Javier;  Martínez, Francisco Luis Benítez;  Ferretti, Stefano;  Guardeño, David Arroyo;  Nevado-Batalla, Pedro Tomás (Hrsg.): Blockchain and Applications, 4th International Congress, S. 131–141, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-21229-1.
Abstract | BibTeX | Schlagwörter: 
@inproceedings{10.1007/978-3-031-21229-1_13,

title = {Prototyping a Smart Contract Application for Fair Reward Distribution in Software Development Projects},

author = {Agostino Di Dia and Tim Riebner and Alexander Arntz and Marc Jansen},

editor = {Javier Prieto and Francisco Luis Benítez Martínez and Stefano Ferretti and David Arroyo Guardeño and Pedro Tomás Nevado-Batalla},

isbn = {978-3-031-21229-1},

year  = {2023},

date = {2023-01-01},

booktitle = {Blockchain and Applications, 4th International Congress},

pages = {131--141},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The following work describes the development of a project management platform based on the waves blockchain technology. The aim is to use the platform to optimally reward both fairness and the achievement of quality standards in software development. Due to the outsourcing of software projects and the associated processes, situations arise in which different project participants work on the same tasks, while paid differently and in most cases independent of the delivered software quality. This is based on the respective contract conditions an individual software developer has negotiated, without factoring in the actual quality of the code the developer provided. The proposal of this work is a prototypical software application that takes the requirements of a project and measures the corresponding contributions of the developers based on their software quality. Project sponsors can also pay out the enrolled project partners via the platform.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
The following work describes the development of a project management platform based on the waves blockchain technology. The aim is to use the platform to optimally reward both fairness and the achievement of quality standards in software development. Due to the outsourcing of software projects and the associated processes, situations arise in which different project participants work on the same tasks, while paid differently and in most cases independent of the delivered software quality. This is based on the respective contract conditions an individual software developer has negotiated, without factoring in the actual quality of the code the developer provided. The proposal of this work is a prototypical software application that takes the requirements of a project and measures the corresponding contributions of the developers based on their software quality. Project sponsors can also pay out the enrolled project partners via the platform.
Schließen
780.
 Baker, Nermeen Abou;  Handmann, Uwe
E-Waste Recycling Gets Smarter with Digitalization Konferenz 
10th IEEE Conference on Technologies for Sustainability (SUSTECH 2023), IEEE, Portland, Oregon USA, 2023.
BibTeX | Schlagwörter: 
@conference{SUSTECH-BakHan2023,

title = {E-Waste Recycling Gets Smarter with Digitalization},

author = {Nermeen Abou Baker and Uwe Handmann},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {10th IEEE Conference on Technologies for Sustainability (SUSTECH 2023)},

publisher = {IEEE},

address = {Portland, Oregon USA},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Schließen
779.
 Handmann, Uwe;  Baker, Nermeen Abou
Digitalization & Circular Economy (Poster) Konferenz 
HRW, 2023.
BibTeX | Schlagwörter: 
@conference{HN-NRW-HanBak2023,

title = {Digitalization & Circular Economy (Poster)},

author = {Uwe Handmann and Nermeen Abou Baker},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {HRW},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Schließen
2022
778.
 Grün, Felix;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis
Invariance to Quantile Selection in Distributional Continuous Control Artikel 
In: arXiv:2212.14262 [cs.LG], 2022.
Links | BibTeX | Schlagwörter: Artificial Intelligence (cs.AI), FOS: Computer and information sciences, I.2.6, I.2.8, Machine Learning, Machine Learning (cs.LG)
@article{grunInvarianceQuantileSelection2022,

title = {Invariance to Quantile Selection in Distributional Continuous Control},

author = {Felix Grün and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

url = {https://arxiv.org/abs/2212.14262},

doi = {10.48550/ARXIV.2212.14262},

year  = {2022},

date = {2022-12-29},

urldate = {2022-12-29},

journal = {arXiv:2212.14262 [cs.LG]},

keywords = {Artificial Intelligence (cs.AI), FOS: Computer and information sciences, I.2.6, I.2.8, Machine Learning, Machine Learning (cs.LG)},

pubstate = {published},

tppubtype = {article}

}

Schließen
https://arxiv.org/abs/2212.14262
doi:10.48550/ARXIV.2212.14262
Schließen
777.
 Lehmler, Stephan Johann;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis
Deep transfer learning compared to subject-specific models for sEMG decoders Artikel 
In: Journal of Neural Engineering, Bd. 19, Nr. 5, 2022.
Abstract | Links | BibTeX | Schlagwörter: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning
@article{lehmlerTransferLearningPatientSpecific2021bb,

title = {Deep transfer learning compared to subject-specific models for sEMG decoders},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

editor = {{IOP Publishing},

url = {https://dx.doi.org/10.1088/1741-2552/ac9860},

doi = {10.1088/1741-2552/ac9860},

year  = {2022},

date = {2022-10-28},

urldate = {2022-10-28},

journal = {Journal of Neural Engineering},

volume = {19},

number = {5},

abstract = {{Objective. Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces and their application e.g. rehabilitation therapy. sEMG signals have high inter-subject variability, due to various factors, including skin thickness, body fat percentage, and electrode placement. Deep learning algorithms require long training time and tend to overfit if only few samples are available. In this study, we aim to investigate methods to calibrate deep learning models to a new user when only a limited amount of training data is available. Approach. Two methods are commonly used in the literature, subject-specific modeling and transfer learning. In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on new subjects data and compare their performance to subject-specific models. We evaluate two models on three publicly available databases (non invasive adaptive prosthetics database 2–4) and compare the performance of both calibration schemes in terms of accuracy, required training data, and calibration time. Main results. On average over all settings, our transfer learning approach improves 5%-points on the pretrained models without fine-tuning, and 12%-points on the subject-specific models, while being trained for 22% fewer epochs on average. Our results indicate that transfer learning enables faster learning on fewer training samples than user-specific models. Significance. To the best of our knowledge, this is the first comparison of subject-specific modeling and transfer learning. These approaches are ubiquitously used in the field of sEMG decoding. But the lack of comparative studies until now made it difficult for scientists to assess appropriate calibration schemes. Our results guide engineers evaluating similar use cases.},

keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning},

pubstate = {published},

tppubtype = {article}

}

Schließen
{Objective. Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces and their application e.g. rehabilitation therapy. sEMG signals have high inter-subject variability, due to various factors, including skin thickness, body fat percentage, and electrode placement. Deep learning algorithms require long training time and tend to overfit if only few samples are available. In this study, we aim to investigate methods to calibrate deep learning models to a new user when only a limited amount of training data is available. Approach. Two methods are commonly used in the literature, subject-specific modeling and transfer learning. In this study, we investigate the effectiveness of transfer learning using weight initialization for recalibration of two different pretrained deep learning models on new subjects data and compare their performance to subject-specific models. We evaluate two models on three publicly available databases (non invasive adaptive prosthetics database 2–4) and compare the performance of both calibration schemes in terms of accuracy, required training data, and calibration time. Main results. On average over all settings, our transfer learning approach improves 5%-points on the pretrained models without fine-tuning, and 12%-points on the subject-specific models, while being trained for 22% fewer epochs on average. Our results indicate that transfer learning enables faster learning on fewer training samples than user-specific models. Significance. To the best of our knowledge, this is the first comparison of subject-specific modeling and transfer learning. These approaches are ubiquitously used in the field of sEMG decoding. But the lack of comparative studies until now made it difficult for scientists to assess appropriate calibration schemes. Our results guide engineers evaluating similar use cases.
Schließen
https://dx.doi.org/10.1088/1741-2552/ac9860
Schließen
776.
 Grün, Felix;  Iossifidis, Ioannis
Exploring Distribution Parameterizations for Distributional Continuous Control Proceedings Article 
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning
@inproceedings{grunExploringDistributionParameterizations2022,

title = {Exploring Distribution Parameterizations for Distributional Continuous Control},

author = {Felix Grün and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2022.112},

year  = {2022},

date = {2022-09-15},

urldate = {2022-09-15},

booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2022.112
Schließen
775.
 Lehmler, Stephan Johann;  Saif-ur-Rehman, Muhammad;  Iossifidis, Ioannis
Modeling Subject Specfic Surface EMG Features by Means of Deep Learning Proceedings Article 
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{lehmlerModelingSubjectSpecfic2022,

title = {Modeling Subject Specfic Surface EMG Features by Means of Deep Learning},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2022.309},

year  = {2022},

date = {2022-09-15},

urldate = {2022-09-15},

booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2022.309
Schließen
774.
 Schmidt, Marie Dominique;  Iossifidis, Ioannis
Linking Muscle Activity and Motion Trajectory Proceedings Article 
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{schmidtLinkingMuscleActivity2022,

title = {Linking Muscle Activity and Motion Trajectory},

author = {Marie Dominique Schmidt and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2022.191},

year  = {2022},

date = {2022-09-15},

urldate = {2022-09-15},

booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2022.191
Schließen
773.
 Sziburis, Tim;  Blex, Susanne;  Iossifidis, Ioannis
A Dataset of 3D Hand Transport Trajectories Determined by Inertial Measurements from a Single Sensor Proceedings Article 
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{sziburisDataset3DHand2022,

title = {A Dataset of 3D Hand Transport Trajectories Determined by Inertial Measurements from a Single Sensor},

author = {Tim Sziburis and Susanne Blex and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2022.186},

year  = {2022},

date = {2022-09-15},

urldate = {2022-09-15},

booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2022.186
Schließen
772.
 Fidencio, Aline Xavier;  Klaes, Christian;  Iossifidis, Ioannis
Closed-Loop Adaptation of Brain-Machine Interfaces Using Error-Related Potentials and Reinforcement Learning Proceedings Article 
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{xavierfidencioClosedloopAdaptationBrainmachine2022,

title = {Closed-Loop Adaptation of Brain-Machine Interfaces Using Error-Related Potentials and Reinforcement Learning},

author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2022.136},

year  = {2022},

date = {2022-09-15},

urldate = {2022-09-15},

booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2022.136
Schließen
771.
 Doliwa, Sebastian;  Hussain, Muhammad Ayaz;  Sziburis, Tim;  Iossifidis, Ioannis
Biologically Inspired Model for Timed Motion in Robotic Systems Proceedings Article 
In: 9th IEEE RAS/EMBS International Conference on Biomedical Robotics & Biomechatronics, IEEE, Seoul, South Korea, 2022.
BibTeX | Schlagwörter: Autonomous robotics, Dynamical systems
@inproceedings{doliwaBiologicallyInspiredModel2022,

title = {Biologically Inspired Model for Timed Motion in Robotic Systems},

author = {Sebastian Doliwa and Muhammad Ayaz Hussain and Tim Sziburis and Ioannis Iossifidis},

year  = {2022},

date = {2022-08-12},

urldate = {2022-08-12},

booktitle = {9th IEEE RAS/EMBS International Conference on Biomedical Robotics & Biomechatronics},

publisher = {IEEE},

address = {Seoul, South Korea},

keywords = {Autonomous robotics, Dynamical systems},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
770.
 Fidencio, Aline Xavier;  Klaes, Christian;  Iossifidis, Ioannis
Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces Artikel 
In: Frontiers in Human Neuroscience, Bd. 16, 2022.
Abstract | Links | BibTeX | Schlagwörter: BCI, EEG, error-related potentials, Machine Learning, Reinforcement learning
@article{xavierfidencioErrorrelated,

title = {Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces},

author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},

url = {https://www.frontiersin.org/article/10.3389/fnhum.2022.806517},

doi = {https://doi.org/10.3389/fnhum.2022.806517},

year  = {2022},

date = {2022-06-24},

urldate = {2022-06-24},

journal = {Frontiers in Human Neuroscience},

volume = {16},

abstract = {The human brain has been an object of extensive investigation in different fields. While several studies have focused on understanding the neural correlates of error processing, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs information specifically in a reinforcement learning framework to go beyond error correction and have used these signals for learning.},

keywords = {BCI, EEG, error-related potentials, Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {article}

}

Schließen
The human brain has been an object of extensive investigation in different fields. While several studies have focused on understanding the neural correlates of error processing, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs information specifically in a reinforcement learning framework to go beyond error correction and have used these signals for learning.
Schließen
https://www.frontiersin.org/article/10.3389/fnhum.2022.806517
doi:https://doi.org/10.3389/fnhum.2022.806517
Schließen
769.
 Ali, Omair;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis;  Klaes, Christian
ConTraNet: A Single End-to-End Hybrid Network for EEG-based and EMG-based Human Machine Interfaces Artikel 
In: 2022.
Abstract | Links | BibTeX | Schlagwörter: BCI, Machine Learning, neural processing, signal processing
@article{aliConTraNetSingleEndtoend2022,

title = {ConTraNet: A Single End-to-End Hybrid Network for EEG-based and EMG-based Human Machine Interfaces},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

url = {http://arxiv.org/abs/2206.10677},

doi = {10.48550/arXiv.2206.10677},

year  = {2022},

date = {2022-06-21},

urldate = {2022-06-21},

abstract = {Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI and EMG-HMI paradigm) for the rehabilitation of physically disabled people. Successful decoding of EEG and EMG signals into respective control command is a pivotal step in the rehabilitation process. Recently, several Convolutional neural networks (CNNs) based architectures are proposed that directly map the raw time-series signal into decision space and the process of meaningful features extraction and classification are performed simultaneously. However, these networks are tailored to the learn the expected characteristics of the given bio-signal and are limited to single paradigm. In this work, we addressed the question that can we build a single architecture which is able to learn distinct features from different HMI paradigms and still successfully classify them. Approach: In this work, we introduce a single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that is equally useful for EEG-HMI and EMG-HMI paradigms. ConTraNet uses CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the long-range dependencies in the signal, which are crucial for the classification of EEG and EMG signals. Main results: We evaluated and compared the ConTraNet with state-of-the-art methods on three publicly available datasets which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, and 10-class decoding tasks). Significance: The results suggest that ConTraNet is robust to learn distinct features from different HMI paradigms and generalizes well as compared to the current state of the art algorithms.},

keywords = {BCI, Machine Learning, neural processing, signal processing},

pubstate = {published},

tppubtype = {article}

}

Schließen
Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI and EMG-HMI paradigm) for the rehabilitation of physically disabled people. Successful decoding of EEG and EMG signals into respective control command is a pivotal step in the rehabilitation process. Recently, several Convolutional neural networks (CNNs) based architectures are proposed that directly map the raw time-series signal into decision space and the process of meaningful features extraction and classification are performed simultaneously. However, these networks are tailored to the learn the expected characteristics of the given bio-signal and are limited to single paradigm. In this work, we addressed the question that can we build a single architecture which is able to learn distinct features from different HMI paradigms and still successfully classify them. Approach: In this work, we introduce a single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that is equally useful for EEG-HMI and EMG-HMI paradigms. ConTraNet uses CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the long-range dependencies in the signal, which are crucial for the classification of EEG and EMG signals. Main results: We evaluated and compared the ConTraNet with state-of-the-art methods on three publicly available datasets which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, and 10-class decoding tasks). Significance: The results suggest that ConTraNet is robust to learn distinct features from different HMI paradigms and generalizes well as compared to the current state of the art algorithms.
Schließen
http://arxiv.org/abs/2206.10677
doi:10.48550/arXiv.2206.10677
Schließen
768.
 Doliwa, Sebastian;  Erbeslöh, Andreas;  Seidl, Karsten;  Iossifidis, Ioannis
Development of a Scalable Analog Front-End for Brain-Computer Interfaces Proceedings Article 
In: 17th International Conference on PhD Research in Microelectronics and Electronics, IEEE Prime 2022, Sardinia, Italy, 2022.
BibTeX | Schlagwörter: BCI, Implantable BCI
@inproceedings{doliwaDevelopmentScalableAnalogaccepted,

title = {Development of a Scalable Analog Front-End for Brain-Computer Interfaces},

author = {Sebastian Doliwa and Andreas Erbeslöh and Karsten Seidl and Ioannis Iossifidis},

year  = {2022},

date = {2022-06-15},

urldate = {2022-06-15},

booktitle = {17th International Conference on PhD Research in Microelectronics and Electronics},

publisher = {IEEE Prime 2022},

address = {Sardinia, Italy},

keywords = {BCI, Implantable BCI},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
767.
 Ali, Omair;  Saif-ur-Rehman, Muhammad;  Dyck, Susanne;  Glasmachers, Tobias;  Iossifidis, Ioannis;  Klaes, Christian
Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method Artikel 
In: Nature Scientific Reports, Bd. 12, Ausg. 1, S. 4245, 2022, ISSN: 2045-2322.
Abstract | Links | BibTeX | Schlagwörter: Adversarial NN, BCI, computer science, EEG, Machine Learning, Quantitative Biology, Quantitative Methods
@article{aliAnchoredSTFTGNAAExtension2021a,

title = {Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method},

author = {Omair Ali and Muhammad Saif-ur-Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},

url = {https://www.nature.com/articles/s41598-022-07992-w},

doi = {https://doi.org/10.1038/s41598-022-07992-w},

issn = {2045-2322},

year  = {2022},

date = {2022-03-10},

urldate = {2022-03-10},

journal = {Nature Scientific Reports},

volume = {12},

issue = {1},

pages = {4245},

abstract = {Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.},

keywords = {Adversarial NN, BCI, computer science, EEG, Machine Learning, Quantitative Biology, Quantitative Methods},

pubstate = {published},

tppubtype = {article}

}

Schließen
Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.
Schließen
https://www.nature.com/articles/s41598-022-07992-w
doi:https://doi.org/10.1038/s41598-022-07992-w
Schließen
766.
 Schmidt, Marie Dominique;  Glasmachers, Tobias;  Iossifidis, Ioannis
From Motion to Muscle Artikel 
In: arXiv: 2201.11501 [cs.LG], 2022.
Links | BibTeX | Schlagwörter: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@article{schmidt2022motion,

title = {From Motion to Muscle},

author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

doi = {https://doi.org/10.48550/arXiv.2201.11501},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {arXiv: 2201.11501 [cs.LG]},

keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},

pubstate = {published},

tppubtype = {article}

}

Schließen
doi:https://doi.org/10.48550/arXiv.2201.11501
Schließen
765.
 Fidencio, Aline Xavier;  Glasmachers, Tobias;  Iossifidis, Ioannis
Error-Related Potentials Detection with Dry- and Wet-Electrode EEG Proceedings Article 
In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022.
Abstract | BibTeX | Schlagwörter: BCI, EEG, error-related potentials, Machine Learning
@inproceedings{fidencioErrorrelatedPotentialsDetection2022,

title = {Error-Related Potentials Detection with Dry- and Wet-Electrode EEG},

author = {Aline Xavier Fidencio and Tobias Glasmachers and Ioannis Iossifidis},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {FENS, Forum 2022},

publisher = {FENS, Federation of European Neuroscience Societies},

abstract = {Electroencephalography (EEG) is a non-invasive technique for measuring brain electrical activity from electrodes placed on the scalp surface. Improvements in this technology are particularly relevant because they also boost brain-machine interfaces (BMI) development. Commonly, gel-based electrodes are used since they guarantee a high-quality signal. Alternatively, dry electrodes have been introduced, more suitable for daily use. In this work, we compare conventional dry and wet electrode systems specifically for the detection of error-related potentials (ErrPs). ErrPs are elicited as a reaction to both self-made and external errors. There has been increased interest in the integration of these signals into BMIs to improve their performance since they provide a convenient source of feedback to the system with no extra workload for the subject. These signals can be used, e.g., to correct errors or even for system adaptation. ErrP-based BMIs in the literature have consistently used wet electrodes. Therefore, even though both electrodes types have been compared for other event-related potentials (e.g., P300), it is relevant to know whether the signal quality for the detection of ErrPs is comparable among them. In this work, we implement a simple game to elicit ErrPs and compare the quality of the measured signals. We tested the feasibility of the experimental protocol to elicit ErrP and the measured ErrP displayed a similar waveshape in terms of observed peaks. However, differences exist in both latencies as well as in their amplitude. These variations and other relevant characteristics have to be further verified with more subjects},

keywords = {BCI, EEG, error-related potentials, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Electroencephalography (EEG) is a non-invasive technique for measuring brain electrical activity from electrodes placed on the scalp surface. Improvements in this technology are particularly relevant because they also boost brain-machine interfaces (BMI) development. Commonly, gel-based electrodes are used since they guarantee a high-quality signal. Alternatively, dry electrodes have been introduced, more suitable for daily use. In this work, we compare conventional dry and wet electrode systems specifically for the detection of error-related potentials (ErrPs). ErrPs are elicited as a reaction to both self-made and external errors. There has been increased interest in the integration of these signals into BMIs to improve their performance since they provide a convenient source of feedback to the system with no extra workload for the subject. These signals can be used, e.g., to correct errors or even for system adaptation. ErrP-based BMIs in the literature have consistently used wet electrodes. Therefore, even though both electrodes types have been compared for other event-related potentials (e.g., P300), it is relevant to know whether the signal quality for the detection of ErrPs is comparable among them. In this work, we implement a simple game to elicit ErrPs and compare the quality of the measured signals. We tested the feasibility of the experimental protocol to elicit ErrP and the measured ErrP displayed a similar waveshape in terms of observed peaks. However, differences exist in both latencies as well as in their amplitude. These variations and other relevant characteristics have to be further verified with more subjects
Schließen
764.
 Schmidt, Marie Dominique;  Glasmachers, Tobias;  Iossifidis, Ioannis
Motion Intention Prediction Proceedings Article 
In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022.
Abstract | BibTeX | Schlagwörter: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@inproceedings{schmidtMotionIntentionPrediction2022a,

title = {Motion Intention Prediction},

author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {FENS, Forum 2022},

publisher = {FENS, Federation of European Neuroscience Societies},

abstract = {Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.},

keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.
Schließen
763.
 Helgert, André;  Zielinska, Laura;  Groeneveld, Anna;  Kloos, Chiara;  Arntz, Alexander;  Straßmann, Carolin;  Eimler, Sabrina C.
DiSensity: Ein hochschulweites Virtual Reality Sensibilisierungsprogramm Proceedings Article 
In:  Söbke, Heinrich;  Zender, Raphael (Hrsg.): Wettbewerbsband AVRiL 2022, S. 17-22, Gesellschaft für Informatik e.V., Bonn, 2022.
Links | BibTeX | Schlagwörter: 
@inproceedings{mci/Helgert2022,

title = {DiSensity: Ein hochschulweites Virtual Reality Sensibilisierungsprogramm},

author = {André Helgert and Laura Zielinska and Anna Groeneveld and Chiara Kloos and Alexander Arntz and Carolin Straßmann and Sabrina C. Eimler},

editor = {Heinrich Söbke and Raphael Zender},

doi = {10.18420/avril2022_03},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Wettbewerbsband AVRiL 2022},

pages = {17-22},

publisher = {Gesellschaft für Informatik e.V.},

address = {Bonn},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.18420/avril2022_03
Schließen
762.
 Straßmann, Carolin;  Eimler, Sabrina C.;  Kololli, Linda;  Arntz, Alexander;  Sand, Katharina;  Rietz, Annika
Effects of the Surroundings in Human-Robot Interaction: Stereotypical Perception of Robots and Its Anthropomorphism Proceedings Article 
In:  Salvendy, Gavriel;  Wei, June (Hrsg.): Design, Operation and Evaluation of Mobile Communications, S. 363–377, Springer International Publishing, Cham, 2022, ISBN: 978-3-031-05014-5.
Abstract | BibTeX | Schlagwörter: 
@inproceedings{10.1007/978-3-031-05014-5_30,

title = {Effects of the Surroundings in Human-Robot Interaction: Stereotypical Perception of Robots and Its Anthropomorphism},

author = {Carolin Straßmann and Sabrina C. Eimler and Linda Kololli and Alexander Arntz and Katharina Sand and Annika Rietz},

editor = {Gavriel Salvendy and June Wei},

isbn = {978-3-031-05014-5},

year  = {2022},

date = {2022-01-01},

booktitle = {Design, Operation and Evaluation of Mobile Communications},

pages = {363--377},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Stereotypes and scripts guide human perception and expectations in everyday life. Research has found that a robot's appearance influences the perceived fit in different application domains (e.g. industrial or social) and that the role a robot is presented in predicts its perceived personality. However, it is unclear how the surroundings as such can elicit a halo effect leading to stereotypical perceptions. This paper presents the results of an experimental study in which 206 participants saw 8 cartoon pictures of the robot Pepper in different application domains in a within-subjects online study. Results indicate that the environment a robot is placed in has an effect on the users' evaluation of the robot's warmth, competence, status in society, competition, anthropomorphism, and morality. As the first impression has an effect on users' expectations and evaluation of the robot and the interaction with it, the effect of the application scenarios has to be considered carefully.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Stereotypes and scripts guide human perception and expectations in everyday life. Research has found that a robot's appearance influences the perceived fit in different application domains (e.g. industrial or social) and that the role a robot is presented in predicts its perceived personality. However, it is unclear how the surroundings as such can elicit a halo effect leading to stereotypical perceptions. This paper presents the results of an experimental study in which 206 participants saw 8 cartoon pictures of the robot Pepper in different application domains in a within-subjects online study. Results indicate that the environment a robot is placed in has an effect on the users' evaluation of the robot's warmth, competence, status in society, competition, anthropomorphism, and morality. As the first impression has an effect on users' expectations and evaluation of the robot and the interaction with it, the effect of the application scenarios has to be considered carefully.
Schließen
761.
 Arntz, Alexander;  Adler, Felix;  Kitzmann, Dennis;  Eimler, Sabrina C.
Augmented Reality Supported Real-Time Data Processing Using Internet of Things Sensor Technology Proceedings Article 
In:  Streitz, Norbert A.;  Konomi, Shin'ichi (Hrsg.): Distributed, Ambient and Pervasive Interactions. Smart Living, Learning, Well-being and Health, Art and Creativity, S. 3–17, Springer International Publishing, Cham, 2022, ISBN: 978-3-031-05431-0.
Abstract | BibTeX | Schlagwörter: 
@inproceedings{10.1007/978-3-031-05431-0_1,

title = {Augmented Reality Supported Real-Time Data Processing Using Internet of Things Sensor Technology},

author = {Alexander Arntz and Felix Adler and Dennis Kitzmann and Sabrina C. Eimler},

editor = {Norbert A. Streitz and Shin'ichi Konomi},

isbn = {978-3-031-05431-0},

year  = {2022},

date = {2022-01-01},

booktitle = {Distributed, Ambient and Pervasive Interactions. Smart Living, Learning, Well-being and Health, Art and Creativity},

pages = {3--17},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Internet of things (IoT) devices increasingly permeate everyday life and provide vital and convenient information. Augmented reality (AR) enables the embedding of this information in the environment using visualizations that can contextualize data for various applications such as Smart Home. Current applications providing a visual representation of the information are often limited to graphs or bar charts, neglecting the variety of possible coherence between the subject and the visualization. We present a setup for real-time AR-based visualizations of data collected by IoT devices. Three distinct battery-powered IoT microcontroller systems were designed and programmed. Each is outfitted with numerous sensors, i.e. for humidity or temperature, to interact with the developed AR application through a network connection. The AR application was developed using Unity3D and the Vuforia AR SDK for Android-based mobile devices with the goal of providing processed and visualized information that is comprehensible for the respective context. Inspired by weather applications for mobile devices, the visualization contains animated dioramas, with changing attributes based on the input data from the IoT microcontroller. This work contains the configuration of the IoT microcontroller hardware, the network interface used, the development process of the AR application, and its usage, complemented by possible future extensions described in an outlook.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Internet of things (IoT) devices increasingly permeate everyday life and provide vital and convenient information. Augmented reality (AR) enables the embedding of this information in the environment using visualizations that can contextualize data for various applications such as Smart Home. Current applications providing a visual representation of the information are often limited to graphs or bar charts, neglecting the variety of possible coherence between the subject and the visualization. We present a setup for real-time AR-based visualizations of data collected by IoT devices. Three distinct battery-powered IoT microcontroller systems were designed and programmed. Each is outfitted with numerous sensors, i.e. for humidity or temperature, to interact with the developed AR application through a network connection. The AR application was developed using Unity3D and the Vuforia AR SDK for Android-based mobile devices with the goal of providing processed and visualized information that is comprehensible for the respective context. Inspired by weather applications for mobile devices, the visualization contains animated dioramas, with changing attributes based on the input data from the IoT microcontroller. This work contains the configuration of the IoT microcontroller hardware, the network interface used, the development process of the AR application, and its usage, complemented by possible future extensions described in an outlook.
Schließen
760.
 Dia, Agostino Di;  Riebner, Tim;  Arntz, Alexander;  Eimler, Sabrina C.
Augmented-Reality-Based Real-Time Patient Information for Nursing Proceedings Article 
In:  Yamamoto, Sakae;  Mori, Hirohiko (Hrsg.): Human Interface and the Management of Information: Applications in Complex Technological Environments, S. 195–208, Springer International Publishing, Cham, 2022, ISBN: 978-3-031-06509-5.
Abstract | BibTeX | Schlagwörter: 
@inproceedings{10.1007/978-3-031-06509-5_14,

title = {Augmented-Reality-Based Real-Time Patient Information for Nursing},

author = {Agostino Di Dia and Tim Riebner and Alexander Arntz and Sabrina C. Eimler},

editor = {Sakae Yamamoto and Hirohiko Mori},

isbn = {978-3-031-06509-5},

year  = {2022},

date = {2022-01-01},

booktitle = {Human Interface and the Management of Information: Applications in Complex Technological Environments},

pages = {195--208},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {While the usage of digital systems in the medical sector has increased, nursing activities are still mostly performed without any form of digital assistance. Considering the complex and demanding procedures the medical personnel is confronted with, a high task load is expected which is prone to human errors. Solutions, however, need to match staff requirements and ideally involve them in the development process to ensure acceptance and usage. Based on desired application scenarios, we introduce a concept of an augmented reality (AR)-based patient data application that provides context-relevant information for nursing staff and doctors. Developed for the Hololens 2, the application allows the retrieval and synchronization of the patient data from the host network of the respective hospital information system. For this purpose, a system infrastructure consisting of several software components was developed to simulate the exchange between the AR device and the independent hospital environment. The paper outlines the conceptual approach based on requirements collected from nurses, related work, the technical implementation and discusses limitations and future developments.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
While the usage of digital systems in the medical sector has increased, nursing activities are still mostly performed without any form of digital assistance. Considering the complex and demanding procedures the medical personnel is confronted with, a high task load is expected which is prone to human errors. Solutions, however, need to match staff requirements and ideally involve them in the development process to ensure acceptance and usage. Based on desired application scenarios, we introduce a concept of an augmented reality (AR)-based patient data application that provides context-relevant information for nursing staff and doctors. Developed for the Hololens 2, the application allows the retrieval and synchronization of the patient data from the host network of the respective hospital information system. For this purpose, a system infrastructure consisting of several software components was developed to simulate the exchange between the AR device and the independent hospital environment. The paper outlines the conceptual approach based on requirements collected from nurses, related work, the technical implementation and discusses limitations and future developments.
Schließen
759.
 Arntz, Alexander;  Straßmann, Carolin;  Völker, Stefanie;  Eimler, Sabrina C.
Collaborating eye to eye: Effects of workplace design on the perception of dominance of collaboration robots Artikel 
In: Frontiers in Robotics and AI, Bd. 9, 2022, ISSN: 2296-9144.
Abstract | Links | BibTeX | Schlagwörter: 
@article{10.3389/frobt.2022.999308,

title = {Collaborating eye to eye: Effects of workplace design on the perception of dominance of collaboration robots},

author = {Alexander Arntz and Carolin Straßmann and Stefanie Völker and Sabrina C. Eimler},

url = {https://www.frontiersin.org/articles/10.3389/frobt.2022.999308},

doi = {10.3389/frobt.2022.999308},

issn = {2296-9144},

year  = {2022},

date = {2022-01-01},

journal = {Frontiers in Robotics and AI},

volume = {9},

abstract = {The concept of Human-Robot Collaboration (HRC) describes innovative industrial work procedures, in which human staff works in close vicinity with robots on a shared task. Current HRC scenarios often deploy hand-guided robots or remote controls operated by the human collaboration partner. As HRC envisions active collaboration between both parties, ongoing research efforts aim to enhance the capabilities of industrial robots not only in the technical dimension but also in the robot’s socio-interactive features. Apart from enabling the robot to autonomously complete the respective shared task in conjunction with a human partner, one essential aspect lifted from the group collaboration among humans is the communication between both entities. State-of-the-art research has identified communication as a significant contributor to successful collaboration between humans and industrial robots. Non-verbal gestures have been shown to be contributing aspect in conveying the respective state of the robot during the collaboration procedure. Research indicates that, depending on the viewing perspective, the usage of non-verbal gestures in humans can impact the interpersonal attribution of certain characteristics. Applied to collaborative robots such as the Yumi IRB 14000, which is equipped with two arms, specifically to mimic human actions, the perception of the robots’ non-verbal behavior can affect the collaboration. Most important in this context are dominance emitting gestures by the robot that can reinforce negative attitudes towards robots, thus hampering the users’ willingness and effectiveness to collaborate with the robot. By using a 3 × 3 within-subjects design online study, we investigated the effect of dominance gestures (Akimbo, crossing arms, and large arm spread) working in a standing position with an average male height, working in a standing position with an average female height, and working in a seated position on the perception of dominance of the robot. Overall 115 participants (58 female and 57 male) with an average age of 23 years evaluated nine videos of the robot. Results indicated that all presented gestures affect a person’s perception of the robot in regards to its perceived characteristics and willingness to cooperate with the robot. The data also showed participants’ increased attribution of dominance based on the presented viewing perspective.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Schließen
The concept of Human-Robot Collaboration (HRC) describes innovative industrial work procedures, in which human staff works in close vicinity with robots on a shared task. Current HRC scenarios often deploy hand-guided robots or remote controls operated by the human collaboration partner. As HRC envisions active collaboration between both parties, ongoing research efforts aim to enhance the capabilities of industrial robots not only in the technical dimension but also in the robot’s socio-interactive features. Apart from enabling the robot to autonomously complete the respective shared task in conjunction with a human partner, one essential aspect lifted from the group collaboration among humans is the communication between both entities. State-of-the-art research has identified communication as a significant contributor to successful collaboration between humans and industrial robots. Non-verbal gestures have been shown to be contributing aspect in conveying the respective state of the robot during the collaboration procedure. Research indicates that, depending on the viewing perspective, the usage of non-verbal gestures in humans can impact the interpersonal attribution of certain characteristics. Applied to collaborative robots such as the Yumi IRB 14000, which is equipped with two arms, specifically to mimic human actions, the perception of the robots’ non-verbal behavior can affect the collaboration. Most important in this context are dominance emitting gestures by the robot that can reinforce negative attitudes towards robots, thus hampering the users’ willingness and effectiveness to collaborate with the robot. By using a 3 × 3 within-subjects design online study, we investigated the effect of dominance gestures (Akimbo, crossing arms, and large arm spread) working in a standing position with an average male height, working in a standing position with an average female height, and working in a seated position on the perception of dominance of the robot. Overall 115 participants (58 female and 57 male) with an average age of 23 years evaluated nine videos of the robot. Results indicated that all presented gestures affect a person’s perception of the robot in regards to its perceived characteristics and willingness to cooperate with the robot. The data also showed participants’ increased attribution of dominance based on the presented viewing perspective.
Schließen
https://www.frontiersin.org/articles/10.3389/frobt.2022.999308
doi:10.3389/frobt.2022.999308
Schließen
758.
 Baker, Nermeen Abou;  Stehr, Jonas;  Handmann, Uwe
Transfer Learning Approach towards a Smarter Recycling Konferenz 
31st International Conference on Artificial Neural Networks (ICANN 2022), Bristol, UK. Artificial Neural Networks and Machine Learning. Lecture Notes in Computer Science, vol 13529, Springer, Cham, 2022.
Links | BibTeX | Schlagwörter: 
@conference{ICANN-BakSteHan2022,

title = {Transfer Learning Approach towards a Smarter Recycling},

author = {Nermeen Abou Baker and Jonas Stehr and Uwe Handmann},

url = {https://link.springer.com/chapter/10.1007/978-3-031-15919-0_57},

doi = {10.1007/978-3-031-15919-0_57},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {31st International Conference on Artificial Neural Networks (ICANN 2022), Bristol, UK. Artificial Neural Networks and Machine Learning. Lecture Notes in Computer Science, vol 13529},

pages = {685--696},

address = {Springer, Cham},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Schließen
https://link.springer.com/chapter/10.1007/978-3-031-15919-0_57
doi:10.1007/978-3-031-15919-0_57
Schließen
757.
 Baker, Nermeen Abou;  Handmann, Uwe
An approach for smart and cost-efficient automated E-Waste recycling for small to medium-sized devices using multi-sensors Konferenz 
IEEE Sensors 2022, Dallas, Texas, USA, 2022.
Links | BibTeX | Schlagwörter: 
@conference{Sensors-BakHan2022,

title = {An approach for smart and cost-efficient automated E-Waste recycling for small to medium-sized devices using multi-sensors},

author = {Nermeen Abou Baker and Uwe Handmann},

url = {https://ieeexplore.ieee.org/document/9967195},

doi = {10.1109/SENSORS52175.2022.9967195},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {IEEE Sensors 2022},

address = {Dallas, Texas, USA},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Schließen
https://ieeexplore.ieee.org/document/9967195
doi:10.1109/SENSORS52175.2022.9967195
Schließen
756.
 Baker, Nermeen Abou;  Zengeler, Nico;  Handmann, Uwe
A Transfer Learning Evaluation of Deep Neural Networks for Image Classification Artikel 
In: Machine Learning and Knowledge Extraction, Bd. 4, Nr. 1, S. 22–41, 2022, ISSN: 2504-4990.
Links | BibTeX | Schlagwörter: 
@article{MAKE-BakZenHan2022,

title = {A Transfer Learning Evaluation of Deep Neural Networks for Image Classification},

author = {Nermeen Abou Baker and Nico Zengeler and Uwe Handmann},

url = {https://www.mdpi.com/2504-4990/4/1/2},

doi = {10.3390/make4010002},

issn = {2504-4990},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Machine Learning and Knowledge Extraction},

volume = {4},

number = {1},

pages = {22--41},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Schließen
https://www.mdpi.com/2504-4990/4/1/2
doi:10.3390/make4010002
Schließen
755.
 Zengeler, Nico;  Glasmachers, Tobias;  Handmann, Uwe
Transfer Meta Learning Konferenz 
26TH International Conference on Pattern Recognition (ICPR 2022), Montreal, Canada, 2022.
Links | BibTeX | Schlagwörter: 
@conference{ICPR-ZenGlaHan2022,

title = {Transfer Meta Learning},

author = {Nico Zengeler and Tobias Glasmachers and Uwe Handmann},

url = {https://ieeexplore.ieee.org/document/9956622},

doi = {10.1109/ICPR56361.2022.9956622},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {26TH International Conference on Pattern Recognition (ICPR 2022)},

pages = {4471-4478},

address = {Montreal, Canada},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Schließen
https://ieeexplore.ieee.org/document/9956622
doi:10.1109/ICPR56361.2022.9956622
Schließen
754.
 Baker, Nermeen Abou;  Rohrschneider, David;  Handmann, Uwe
Battery detection of XRay images using transfer learning Konferenz 
The 30th European Symposium on Artificial Neural Networks (ESANN 2022), Bruges, Belgium, 2022.
Links | BibTeX | Schlagwörter: 
@conference{ESANN-BakRohHan2022,

title = {Battery detection of XRay images using transfer learning},

author = {Nermeen Abou Baker and David Rohrschneider and Uwe Handmann},

url = {https://www.esann.org/sites/default/files/proceedings/2022/ES2022-60.pdf},

doi = {10.14428/esann/2022.ES2022-60},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {The 30th European Symposium on Artificial Neural Networks (ESANN 2022)},

pages = {241-246},

address = {Bruges, Belgium},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Schließen
https://www.esann.org/sites/default/files/proceedings/2022/ES2022-60.pdf
doi:10.14428/esann/2022.ES2022-60
Schließen
753.
 Helgert, André;  Straßmann, Carolin
What Are You Grateful for? - Enhancing Gratitude Routines by Using Speech Assistants Proceedings Article 
In: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New Orleans, LA, USA, 2022, ISBN: 9781450391566.
Abstract | Links | BibTeX | Schlagwörter: Alexa, Gratitude Journal, Gratitude Routines, Speech Assistant
@inproceedings{10.1145/3491101.3519786,

title = {What Are You Grateful for? - Enhancing Gratitude Routines by Using Speech Assistants},

author = {André Helgert and Carolin Straßmann},

url = {https://doi.org/10.1145/3491101.3519786},

doi = {10.1145/3491101.3519786},

isbn = {9781450391566},

year  = {2022},

date = {2022-01-01},

booktitle = {Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems},

publisher = {Association for Computing Machinery},

address = {New Orleans, LA, USA},

series = {CHI EA '22},

abstract = {This paper presents an extension for Amazon’s Alexa, which provides a gratitude journal, and investigates its effectiveness compared to a regular paper-based version. Decades of research demonstrate that expressing gratitude has various psychological and physical benefits. At the same time, gratitude routines run the risk of being a hassle activity, which diminishes the positive outcome. Speech assistants might help to integrate gratitude routines more easily in an intuitive way using voice input. The results of our 8-day field study with two experimental groups (Alexa group vs. Paper group},

keywords = {Alexa, Gratitude Journal, Gratitude Routines, Speech Assistant},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
This paper presents an extension for Amazon’s Alexa, which provides a gratitude journal, and investigates its effectiveness compared to a regular paper-based version. Decades of research demonstrate that expressing gratitude has various psychological and physical benefits. At the same time, gratitude routines run the risk of being a hassle activity, which diminishes the positive outcome. Speech assistants might help to integrate gratitude routines more easily in an intuitive way using voice input. The results of our 8-day field study with two experimental groups (Alexa group vs. Paper group
Schließen
https://doi.org/10.1145/3491101.3519786
doi:10.1145/3491101.3519786
Schließen
2021
752.
 Grün, Felix;  Glasmachers, Tobias;  Iossifidis, Ioannis
Off-Policy Continuous Control Using Distributional Reinforcement Learning Proceedings Article 
In: Bernstein Conference, 2021.
Links | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning
@inproceedings{grunOffPolicyContinuousControl2021b,

title = {Off-Policy Continuous Control Using Distributional Reinforcement Learning},

author = {Felix Grün and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p001},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

publisher = {Bernstein Conference},

keywords = {Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p001
Schließen
751.
 Lehmler, Stephan Johann;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis
Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification Proceedings Article 
In: Bernstein Conferen, 2021.
Links | BibTeX | Schlagwörter: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning
@inproceedings{lehmlerTransferLearningPatientSpecific2021b,

title = {Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p005},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

publisher = {Bernstein Conferen},

keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p005
Schließen
750.
 Schmidt, Marie Dominique;  Glasmachers, Tobias;  Iossifidis, Ioannis
Artificially Generated Muscle Signals Proceedings Article 
In: Bernstein Conference, 2021.
Links | BibTeX | Schlagwörter: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@inproceedings{schmidtArtificiallyGeneratedMuscle2021b,

title = {Artificially Generated Muscle Signals},

author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p111},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

publisher = {Bernstein Conference},

keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p111
Schließen
749.
 Sziburis, Tim;  Blex, Susanne;  Glasmachers, Tobias;  Rano, Inaki;  Iossifidis, Ioannis
Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach Proceedings Article 
In: Bernstein Conference, 2021.
Links | BibTeX | Schlagwörter: attractor dynamics approach, human arm motion
@inproceedings{sziburisModellingGenerationHuman2021b,

title = {Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach},

author = {Tim Sziburis and Susanne Blex and Tobias Glasmachers and Inaki Rano and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p078},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

publisher = {Bernstein Conference},

keywords = {attractor dynamics approach, human arm motion},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p078
Schließen
748.
 Fidencio, Aline Xavier;  Glasmachers, Tobias;  Klaes, Christian;  Iossifidis, Ioannis
Beyond Error Correction: Integration of Error-Related Potentials into Brain-Computer Interfaces for Improved Performance Proceedings Article 
In: Bernstein Conference, 2021.
Links | BibTeX | Schlagwörter: BCI, error-related potentials, Machine Learning, Reinforcement learning
@inproceedings{xavierfidencioErrorCorrectionIntegration2021b,

title = {Beyond Error Correction: Integration of Error-Related Potentials into Brain-Computer Interfaces for Improved Performance},

author = {Aline Xavier Fidencio and Tobias Glasmachers and Christian Klaes and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p163},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

publisher = {Bernstein Conference},

keywords = {BCI, error-related potentials, Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p163
Schließen
747.
 Grün, Felix;  Glasmachers, Tobias;  Iossifidis, Ioannis
Off-Policy Continuous Control Using Distributional Reinforcement Learning Proceedings Article 
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Schlagwörter: Machine Learning, Reinforcement learning
@inproceedings{grunOffPolicyContinuousControl2021,

title = {Off-Policy Continuous Control Using Distributional Reinforcement Learning},

author = {Felix Grün and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p001},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {Machine Learning, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p001
Schließen
746.
 Lehmler, Stephan Johann;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis
Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification Proceedings Article 
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{lehmlerTransferLearningPatientSpecific2021,

title = {Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification},

author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p005},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p005
Schließen
745.
 Schmidt, Marie Dominique;  Glasmachers, Tobias;  Iossifidis, Ioannis
Artificially Generated Muscle Signals Proceedings Article 
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Schlagwörter: BCI, Machine Learning
@inproceedings{schmidtArtificiallyGeneratedMuscle2021,

title = {Artificially Generated Muscle Signals},

author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p111},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p111
Schließen
744.
 Sziburis, Tim;  Blex, Susanne;  Glasmachers, Tobias;  Rano, Inaki;  Iossifidis, Ioannis
Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach Proceedings Article 
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Schlagwörter: BCI, Machine Learning, movement model
@inproceedings{sziburisModellingGenerationHuman2021,

title = {Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach},

author = {Tim Sziburis and Susanne Blex and Tobias Glasmachers and Inaki Rano and Ioannis Iossifidis},

doi = {10.12751/nncn.bc2021.p078},

year  = {2021},

date = {2021-09-15},

urldate = {2021-09-15},

booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},

publisher = {BCCN Bernstein Network Computational Network},

keywords = {BCI, Machine Learning, movement model},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.12751/nncn.bc2021.p078
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