Veröffentlichungen - Institut Informatik

anthropomorphic robot arm anthropomorphism arm movement model artificial skin attractor dynamics approach Autonomous robotics BCI behavior generation direct physical interaction Dynamical systems EEG force sensing haptic interface human anatomy human motor behavior human operators intuition inverse kinematics Machine Learning man machine interaction man-machine-interaction manipulators mechanical structure modeling movement model object recognition pervasive design robot manipulator control robot vision robotic assistant sensory channels user modelling 
825 Einträge « ‹ 2 von 17 › » 
2022
775.
 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
774.
 Helgert, André;  Canbulat, Anil;  Lingnau, Andreas;  Straßmann, Carolin
A Framework for Analyzing Interactions in a Video-based Collaborative Learning Environment Proceedings Article 
In: 2022 International Conference on Advanced Learning Technologies (ICALT), S. 125-127, 2022, ISSN: 2161-377X.
Abstract | Links | BibTeX | Schlagwörter: COVID-19;Learning management systems;Pandemics;Distance learning;Collaboration;Collaborative work;Behavioral sciences;Learning Analytics;Computer-supported Collaborative Learning;Social Learning
@inproceedings{9853790,

title = {A Framework for Analyzing Interactions in a Video-based Collaborative Learning Environment},

author = {André Helgert and Anil Canbulat and Andreas Lingnau and Carolin Straßmann},

doi = {10.1109/ICALT55010.2022.00045},

issn = {2161-377X},

year  = {2022},

date = {2022-07-01},

urldate = {2022-07-01},

booktitle = {2022 International Conference on Advanced Learning Technologies (ICALT)},

pages = {125-127},

abstract = {Studying in social isolation is a reality for many students that was further reinforced after the start of the COVID-19 pandemic. Research shows that isolation can lead to decreased learning efficiency and is intensified by the increased asynchronous online teaching during the pandemic. This change is not only challenging for students, but also for teachers, as students do not have a direct communication and feedback channel when learning content is presented in form of pre-recorded videos in a learning management system. In this paper, we present VGather2Learn Analytics, which is an extension to the already existing collaborative learning system VGather2Learn, which makes it possible for teachers to analyse the learning behavior of students in asynchronous video-teaching. The information presented in a dashboard will allow teachers to better understand how students interact while watching learning videos collaboratively and can improve online-teaching.},

keywords = {COVID-19;Learning management systems;Pandemics;Distance learning;Collaboration;Collaborative work;Behavioral sciences;Learning Analytics;Computer-supported Collaborative Learning;Social Learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Studying in social isolation is a reality for many students that was further reinforced after the start of the COVID-19 pandemic. Research shows that isolation can lead to decreased learning efficiency and is intensified by the increased asynchronous online teaching during the pandemic. This change is not only challenging for students, but also for teachers, as students do not have a direct communication and feedback channel when learning content is presented in form of pre-recorded videos in a learning management system. In this paper, we present VGather2Learn Analytics, which is an extension to the already existing collaborative learning system VGather2Learn, which makes it possible for teachers to analyse the learning behavior of students in asynchronous video-teaching. The information presented in a dashboard will allow teachers to better understand how students interact while watching learning videos collaboratively and can improve online-teaching.
Schließen
doi:10.1109/ICALT55010.2022.00045
Schließen
773.
 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
772.
 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
771.
 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
770.
 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
769.
 Helgert, André;  Groeneveld, Anna;  Eimler, Sabrina C.
A Qualitative Analysis of Interaction Techniques in a Virtual Reality Instruction Environment: Experiences From a Case Study Proceedings Article 
In: 2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), S. 171-175, 2022.
Abstract | Links | BibTeX | Schlagwörter: Training;Location awareness;Solid modeling;Navigation;Keyboards;Virtual reality;Usability;virtual reality;interaction mechanics;interaction patterns;think aloud;virtual environment;virtual interactions;accessibility
@inproceedings{10024471,

title = {A Qualitative Analysis of Interaction Techniques in a Virtual Reality Instruction Environment: Experiences From a Case Study},

author = {André Helgert and Anna Groeneveld and Sabrina C. Eimler},

doi = {10.1109/AIVR56993.2022.00034},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)},

pages = {171-175},

abstract = {As they can immerse people into virtual worlds free from external distraction, virtual reality (VR) applications are an important factor in research today and can be useful in various scenarios such as intervention training, simulations and in learning environments. However, many people are not used to interacting and navigating in VR and may not have much prior technical experience, which can become a distracting factor to the content and can influence success negatively. Thus, benefits of VR-environments cannot be fully exploited. Aiming to counteract the problem that people cannot take advantage of the benefits of VR-applications, an instruction room for training representative interaction patterns, i.e. interaction and locomotion mechanics, was designed. Students (N = 12) were videotaped going through six stations of the instruction room with different interaction tasks. They were prompted to think aloud to capture their thoughts (e.g., intuitiveness, usability and expansion ideas) while interacting, and interviewed afterwards for an overall assessment. A qualitative content analysis helped to identify patterns. Interactions like localization field triggers for instructions and answering a quiz were regarded as highly intuitive, giving feedback with a drum keyboard was reported as most entertaining.},

keywords = {Training;Location awareness;Solid modeling;Navigation;Keyboards;Virtual reality;Usability;virtual reality;interaction mechanics;interaction patterns;think aloud;virtual environment;virtual interactions;accessibility},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
As they can immerse people into virtual worlds free from external distraction, virtual reality (VR) applications are an important factor in research today and can be useful in various scenarios such as intervention training, simulations and in learning environments. However, many people are not used to interacting and navigating in VR and may not have much prior technical experience, which can become a distracting factor to the content and can influence success negatively. Thus, benefits of VR-environments cannot be fully exploited. Aiming to counteract the problem that people cannot take advantage of the benefits of VR-applications, an instruction room for training representative interaction patterns, i.e. interaction and locomotion mechanics, was designed. Students (N = 12) were videotaped going through six stations of the instruction room with different interaction tasks. They were prompted to think aloud to capture their thoughts (e.g., intuitiveness, usability and expansion ideas) while interacting, and interviewed afterwards for an overall assessment. A qualitative content analysis helped to identify patterns. Interactions like localization field triggers for instructions and answering a quiz were regarded as highly intuitive, giving feedback with a drum keyboard was reported as most entertaining.
Schließen
doi:10.1109/AIVR56993.2022.00034
Schließen
768.
 Fulantelli, Giovanni;  Taibi, Davide;  Scifo, Lidia;  Schwarze, Veronica;  Eimler, Sabrina C.
Cyberbullying and Cyberhate as Two Interlinked Instances of Cyber-Aggression in Adolescence: A Systematic Review Artikel 
In: Frontiers in Psychology, Bd. 13, 2022, ISSN: 1664-1078.
Abstract | Links | BibTeX | Schlagwörter: 
@article{10.3389/fpsyg.2022.909299,

title = {Cyberbullying and Cyberhate as Two Interlinked Instances of Cyber-Aggression in Adolescence: A Systematic Review},

author = {Giovanni Fulantelli and Davide Taibi and Lidia Scifo and Veronica Schwarze and Sabrina C. Eimler},

url = {https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.909299},

doi = {10.3389/fpsyg.2022.909299},

issn = {1664-1078},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Frontiers in Psychology},

volume = {13},

abstract = {<p>In this paper we present the results of a systematic review aimed at investigating what the literature reports on cyberbullying and cyberhate, whether and to what extent the connection between the two phenomena is made explicit, and whether it is possible to identify overlapping factors in the description of the phenomena. Specifically, for each of the 24 selected papers, we have identified the predictors of cyberbullying behaviors and the consequences of cyberbullying acts on the victims; the same analysis has been carried out with reference to cyberhate. Then, by comparing what emerged from the literature on cyberbullying with what emerged from the literature on cyberhate, we verify to what extent the two phenomena overlap in terms of predictors and consequences. Results show that the cyberhate issue related to adolescents is less investigated than cyberbullying, and most of the papers focusing on one of them do not refer to the other. Nevertheless, by comparing the predictors and outcomes of cyberbullying and cyberhate as reported in the literature, an overlap between the two concepts emerges, with reference to: the parent-child relationship to reduce the risk of cyber-aggression; the link between sexuality and cyber-attacks; the protective role of the families and of good quality friendship relationships; the impact of cyberbullying and cyberhate on adolescents' individuals' well-being and emotions; meaningful analogies between the coping strategies put in practice by victims of cyberbullying and cyberhate. We argue that the results of this review can stimulate a holistic approach for future studies on cyberbullying and cyberhate where the two phenomena are analyzed as two interlinked instances of cyber-aggression. Similarly, prevention and intervention programs on a responsible and safe use of social media should refer to both cyberbullying and cyberhate issues, as they share many predictors as well as consequences on adolescents' wellbeing, thus making it diminishing to afford them separately.</p><sec><title>Systematic Review Registration</title><p><ext-link ext-link-type="uri" xlink:href="http://www.crd.york.ac.uk/PROSPERO" xmlns:xlink="http://www.w3.org/1999/xlink">http://www.crd.york.ac.uk/PROSPERO</ext-link>, identifier: CRD42021239461.</p></sec>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Schließen
<p>In this paper we present the results of a systematic review aimed at investigating what the literature reports on cyberbullying and cyberhate, whether and to what extent the connection between the two phenomena is made explicit, and whether it is possible to identify overlapping factors in the description of the phenomena. Specifically, for each of the 24 selected papers, we have identified the predictors of cyberbullying behaviors and the consequences of cyberbullying acts on the victims; the same analysis has been carried out with reference to cyberhate. Then, by comparing what emerged from the literature on cyberbullying with what emerged from the literature on cyberhate, we verify to what extent the two phenomena overlap in terms of predictors and consequences. Results show that the cyberhate issue related to adolescents is less investigated than cyberbullying, and most of the papers focusing on one of them do not refer to the other. Nevertheless, by comparing the predictors and outcomes of cyberbullying and cyberhate as reported in the literature, an overlap between the two concepts emerges, with reference to: the parent-child relationship to reduce the risk of cyber-aggression; the link between sexuality and cyber-attacks; the protective role of the families and of good quality friendship relationships; the impact of cyberbullying and cyberhate on adolescents' individuals' well-being and emotions; meaningful analogies between the coping strategies put in practice by victims of cyberbullying and cyberhate. We argue that the results of this review can stimulate a holistic approach for future studies on cyberbullying and cyberhate where the two phenomena are analyzed as two interlinked instances of cyber-aggression. Similarly, prevention and intervention programs on a responsible and safe use of social media should refer to both cyberbullying and cyberhate issues, as they share many predictors as well as consequences on adolescents' wellbeing, thus making it diminishing to afford them separately.</p><sec><title>Systematic Review Registration</title><p><ext-link ext-link-type="uri" xlink:href="http://www.crd.york.ac.uk/PROSPERO" xmlns:xlink="http://www.w3.org/1999/xlink">http://www.crd.york.ac.uk/PROSPERO</ext-link>, identifier: CRD42021239461.</p></sec>
Schließen
https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.9092[...]
doi:10.3389/fpsyg.2022.909299
Schließen
767.
 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
766.
 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
765.
 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
764.
 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.
Abstract | 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},

abstract = {Diversitätsbezogene Herausforderungen begegnen den meisten Studierenden, Lehrenden und Mitarbeitenden immer wieder im Hochschulalltag. Manchmal wird uns dies nicht bewusst, da wir nicht ausreichend für das Thema sensibilisiert sind und uns Situationen aus unserer eigenen Perspektive unproblematisch erscheinen oder wir ihnen aus dem Weg gehen, wenn sie uns unangenehm sind. Aus diesen Erfahrungen kann eine Schieflage in der Kommunikation oder der Wahrnehmung der anderen Person entstehen, die Probleme, Konflikte oder ein Gefühl sozialer Isolation erzeugt. In diesem Beitrag wird eine immersive Virtual-Reality-Galerie vorgestellt, welche von Akteur:innen aus den Fach- und Servicebereichen und Studierenden entwickelt wird. Das Ziel ist es, die Sensibilität für Vielfalt und deren Bedeutung im Lehr- und Lerngeschehen hochschulweit und bei allen Akteursgruppen zu steigern. Mit dem Einsatz von multimedialen Inhalten und verschiedenen Interaktionsmechaniken in der virtuellen Welt, kann DiSensity als effiziente, kostengünstige und flexible Alternative zu bisherigen Diversitäts Trainings dienen.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Diversitätsbezogene Herausforderungen begegnen den meisten Studierenden, Lehrenden und Mitarbeitenden immer wieder im Hochschulalltag. Manchmal wird uns dies nicht bewusst, da wir nicht ausreichend für das Thema sensibilisiert sind und uns Situationen aus unserer eigenen Perspektive unproblematisch erscheinen oder wir ihnen aus dem Weg gehen, wenn sie uns unangenehm sind. Aus diesen Erfahrungen kann eine Schieflage in der Kommunikation oder der Wahrnehmung der anderen Person entstehen, die Probleme, Konflikte oder ein Gefühl sozialer Isolation erzeugt. In diesem Beitrag wird eine immersive Virtual-Reality-Galerie vorgestellt, welche von Akteur:innen aus den Fach- und Servicebereichen und Studierenden entwickelt wird. Das Ziel ist es, die Sensibilität für Vielfalt und deren Bedeutung im Lehr- und Lerngeschehen hochschulweit und bei allen Akteursgruppen zu steigern. Mit dem Einsatz von multimedialen Inhalten und verschiedenen Interaktionsmechaniken in der virtuellen Welt, kann DiSensity als effiziente, kostengünstige und flexible Alternative zu bisherigen Diversitäts Trainings dienen.
Schließen
doi:10.18420/avril2022_03
Schließen
763.
 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
762.
 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
761.
 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
760.
 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
759.
 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
758.
 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
757.
 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
756.
 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
755.
 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
754.
 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
753.
 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
752.
 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
751.
 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
750.
 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
749.
 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
748.
 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
747.
 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
746.
 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
745.
 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
Schließen
744.
 Larson, I.;  Schwermer, K.;  Handmann, Uwe
Digital4u: Finde deinen Traumberuf! Artikel 
In: Schulwelt NRW, 2021, ISSN: 2626-823X.
BibTeX | Schlagwörter: 
@article{SchulWeltNRW2021,

title = {Digital4u: Finde deinen Traumberuf!},

author = {I. Larson and K. Schwermer and Uwe Handmann},

issn = {2626-823X},

year  = {2021},

date = {2021-09-01},

urldate = {2021-09-01},

journal = {Schulwelt NRW},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Schließen
743.
 Ali, Omair;  Saif-ur-Rehman, Muhammad;  Dyck, Susanne;  Glasmachers, Tobias;  Iossifidis, Ioannis;  Klaes, Christian
Anchored-STFT and GNAA: An Extension of STFT in Conjunction with an Adversarial Data Augmentation Technique for the Decoding of Neural Signals Artikel 
In: arXiv:2011.14694 [cs, q-bio], 2021.
Abstract | BibTeX | Schlagwörter: BCI, Machine Learning, Quantitative Biology, Quantitative Methods
@article{aliAnchoredSTFTGNAAExtension2021,

title = {Anchored-STFT and GNAA: An Extension of STFT in Conjunction with an Adversarial Data Augmentation Technique for the Decoding of Neural Signals},

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

year  = {2021},

date = {2021-08-01},

urldate = {2021-08-01},

journal = {arXiv:2011.14694 [cs, q-bio]},

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 = {BCI, 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
742.
 Doliwa, Sebastian;  Hussain, Muhammad Ayaz;  Sziburis, Tim;  Iossifidis, Ioannis
Biologically Inspired Model for Timed Motion in Robotic Systems Artikel 
In: arXiv:2106.15864 [cs, math], 2021.
Abstract | BibTeX | Schlagwörter: attractor dynamics approach, Autonomous robotics, Dynamical systems
@article{doliwaBiologicallyInspiredModel2021,

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

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

year  = {2021},

date = {2021-07-01},

urldate = {2021-07-01},

journal = {arXiv:2106.15864 [cs, math]},

abstract = {The goal of this work is the development of a motion model for sequentially timed movement actions in robotic systems under specific consideration of temporal stabilization, that is maintaining an approximately constant overall movement time (isochronous behavior). This is demonstrated both in simulation and on a physical robotic system for the task of intercepting a moving target in three-dimensional space. Motivated from humanoid motion, timing plays a vital role to generate a naturalistic behavior in interaction with the dynamic environment as well as adaptively planning and executing action sequences on-line. In biological systems, many of the physiological and anatomical functions follow a particular level of periodicity and stabilization, which exhibit a certain extent of resilience against external disturbances. A main aspect thereof is stabilizing movement timing against limited perturbations. Especially human arm movement, namely when it is tasked to reach a certain goal point, pose or configuration, shows a stabilizing behavior. This work incorporates the utilization of an extended Kalman filter (EKF) which was implemented to predict the target position while coping with non-linear system dynamics. The periodicity and temporal stabilization in biological systems was artificially generated by a Hopf oscillator, yielding a sinusoidal velocity profile for smooth and repeatable motion.},

keywords = {attractor dynamics approach, Autonomous robotics, Dynamical systems},

pubstate = {published},

tppubtype = {article}

}

Schließen
The goal of this work is the development of a motion model for sequentially timed movement actions in robotic systems under specific consideration of temporal stabilization, that is maintaining an approximately constant overall movement time (isochronous behavior). This is demonstrated both in simulation and on a physical robotic system for the task of intercepting a moving target in three-dimensional space. Motivated from humanoid motion, timing plays a vital role to generate a naturalistic behavior in interaction with the dynamic environment as well as adaptively planning and executing action sequences on-line. In biological systems, many of the physiological and anatomical functions follow a particular level of periodicity and stabilization, which exhibit a certain extent of resilience against external disturbances. A main aspect thereof is stabilizing movement timing against limited perturbations. Especially human arm movement, namely when it is tasked to reach a certain goal point, pose or configuration, shows a stabilizing behavior. This work incorporates the utilization of an extended Kalman filter (EKF) which was implemented to predict the target position while coping with non-linear system dynamics. The periodicity and temporal stabilization in biological systems was artificially generated by a Hopf oscillator, yielding a sinusoidal velocity profile for smooth and repeatable motion.
Schließen
741.
 Sziburis, Tim;  Nowak, Markus;  Brunelli, Davide
Prototype Reduction on sEMG Data for Instance-Based Gesture Learning towards Real-Time Prosthetic Control: Proceedings Article 
In: Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, S. 299–305, SCITEPRESS - Science and Technology Publications, Online Streaming, --- Select a Country ---, 2021, ISBN: 978-989-758-490-9.
Links | BibTeX | Schlagwörter: 
@inproceedings{sziburisPrototypeReductionSEMG2021c,

title = {Prototype Reduction on sEMG Data for Instance-Based Gesture Learning towards Real-Time Prosthetic Control:},

author = {Tim Sziburis and Markus Nowak and Davide Brunelli},

url = {https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0010327502990305},

doi = {10.5220/0010327502990305},

isbn = {978-989-758-490-9},

year  = {2021},

date = {2021-03-26},

urldate = {2021-03-26},

booktitle = {Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies},

pages = {299--305},

publisher = {SCITEPRESS - Science and Technology Publications},

address = {Online Streaming, --- Select a Country ---},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0010327502990305
doi:10.5220/0010327502990305
Schließen
740.
 Fidencio, Aline Xavier;  Glasmachers, Tobias;  Naro, Daniele
Application of Reinforcement Learning to a Mining System Proceedings Article 
In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), S. 000111–000118, 2021.
Abstract | Links | BibTeX | Schlagwörter: Control Applications, Industrial Application, Machine Learning, Machine learning algorithms, Mining Industry, Reinforcement learning
@inproceedings{fidencioApplicationReinforcementLearning2021,

title = {Application of Reinforcement Learning to a Mining System},

author = {Aline Xavier Fidencio and Tobias Glasmachers and Daniele Naro},

doi = {10.1109/SAMI50585.2021.9378663},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)},

pages = {000111--000118},

abstract = {Automation techniques have been widely applied in different industry segments, among others, to increase both productivity and safety. In the mining industry, with the usage of such systems, the operator can be removed from hazardous environments without compromising task execution and it is possible to achieve more efficient and standardized operation. In this work a study case on the application of machine learning algorithms to a mining system example is presented, in which reinforcement learning algorithms were used to solve a control problem. As an example, a machine chain consisting of a Bucket Wheel Excavator, a Belt Wagon and a Hopper Car was used. This system has two material transfer points that need to remain aligned during operation in order to allow continuous material flow. To keep the alignment, the controller makes use of seven degrees of freedom given by slewing, luffing and crawler drives. Experimental tests were done in a simulated environment with two state-of-the-art algorithms, namely Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). The trained agents were evaluated in terms of episode return and length, as well as alignment quality and action values used. Results show that, for the given task, the PPO agent performs quantitatively and qualitatively better than the SAC agent. However, none of the agents were able to completely solve the proposed testing task.},

keywords = {Control Applications, Industrial Application, Machine Learning, Machine learning algorithms, Mining Industry, Reinforcement learning},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Automation techniques have been widely applied in different industry segments, among others, to increase both productivity and safety. In the mining industry, with the usage of such systems, the operator can be removed from hazardous environments without compromising task execution and it is possible to achieve more efficient and standardized operation. In this work a study case on the application of machine learning algorithms to a mining system example is presented, in which reinforcement learning algorithms were used to solve a control problem. As an example, a machine chain consisting of a Bucket Wheel Excavator, a Belt Wagon and a Hopper Car was used. This system has two material transfer points that need to remain aligned during operation in order to allow continuous material flow. To keep the alignment, the controller makes use of seven degrees of freedom given by slewing, luffing and crawler drives. Experimental tests were done in a simulated environment with two state-of-the-art algorithms, namely Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). The trained agents were evaluated in terms of episode return and length, as well as alignment quality and action values used. Results show that, for the given task, the PPO agent performs quantitatively and qualitatively better than the SAC agent. However, none of the agents were able to completely solve the proposed testing task.
Schließen
doi:10.1109/SAMI50585.2021.9378663
Schließen
739.
 Wehrmann, Maximilian;  Zengeler, Nico;  Handmann, Uwe
Observation Time Effects in Reinforcement Learning on Contracts for Difference Artikel 
In: Journal of Risk and Financial Management, Bd. 14, Nr. 2:54, S. 1-15, 2021, ISSN: 1911-8074, (MDPI, Basel, Switzerland).
Links | BibTeX | Schlagwörter: 
@article{Wehrmann2021,

title = {Observation Time Effects in Reinforcement Learning on Contracts for Difference},

author = {Maximilian Wehrmann and Nico Zengeler and Uwe Handmann},

url = {https://www.mdpi.com/1911-8074/14/2/54},

doi = {10.3390/jrfm14020054},

issn = {1911-8074},

year  = {2021},

date = {2021-01-01},

journal = {Journal of Risk and Financial Management},

volume = {14},

number = {2:54},

pages = {1-15},

note = {MDPI, Basel, Switzerland},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Schließen
https://www.mdpi.com/1911-8074/14/2/54
doi:10.3390/jrfm14020054
Schließen
738.
 Lehmler, Stephan Johann;  Saif-ur-Rehman, Muhammad;  Glasmachers, Tobias;  Iossifidis, Ioannis
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification Artikel 
In: 2021.
Links | BibTeX | Schlagwörter: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning
@article{lehmler2021deep,

title = {Deep 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},

url = {https://api.semanticscholar.org/CorpusID:245634948},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

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

pubstate = {published},

tppubtype = {article}

}

Schließen
https://api.semanticscholar.org/CorpusID:245634948
Schließen
737.
 Arntz, Alexander;  Eimler, Sabrina C.;  Keßler, Dustin;  Thomas, Jan;  Helgert, André;  Rehm, Markus;  Graf, Eduard;  Wientzek, Sebastian;  Budur, Burak
Walking on the Bright Sight: Evaluating a Photovoltaics Virtual Reality Education Application Proceedings Article 
In: 2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), S. 295-301, 2021.
Links | BibTeX | Schlagwörter: 
@inproceedings{9644336,

title = {Walking on the Bright Sight: Evaluating a Photovoltaics Virtual Reality Education Application},

author = {Alexander Arntz and Sabrina C. Eimler and Dustin Keßler and Jan Thomas and André Helgert and Markus Rehm and Eduard Graf and Sebastian Wientzek and Burak Budur},

doi = {10.1109/AIVR52153.2021.00063},

year  = {2021},

date = {2021-01-01},

booktitle = {2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)},

pages = {295-301},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.1109/AIVR52153.2021.00063
Schließen
736.
 Helgert, André;  Eimler, Sabrina C.;  Arntz, Alexander
Stop Catcalling - A Virtual Environment Educating Against Street Harassment Proceedings Article 
In: 2021 International Conference on Advanced Learning Technologies (ICALT), S. 419-421, 2021.
Links | BibTeX | Schlagwörter: 
@inproceedings{9499881,

title = {Stop Catcalling - A Virtual Environment Educating Against Street Harassment},

author = {André Helgert and Sabrina C. Eimler and Alexander Arntz},

doi = {10.1109/ICALT52272.2021.00133},

year  = {2021},

date = {2021-01-01},

booktitle = {2021 International Conference on Advanced Learning Technologies (ICALT)},

pages = {419-421},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.1109/ICALT52272.2021.00133
Schließen
735.
 Helgert, André;  Eimler, Sabrina C.;  Arntz, Alexander
Learning About Catcalling: an Interactive Virtual Gallery Concept Raising Awareness for Street Harassment Proceedings Article 
In: 2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), S. 287-294, 2021.
Links | BibTeX | Schlagwörter: 
@inproceedings{9644282,

title = {Learning About Catcalling: an Interactive Virtual Gallery Concept Raising Awareness for Street Harassment},

author = {André Helgert and Sabrina C. Eimler and Alexander Arntz},

doi = {10.1109/AIVR52153.2021.00062},

year  = {2021},

date = {2021-01-01},

booktitle = {2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)},

pages = {287-294},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.1109/AIVR52153.2021.00062
Schließen
734.
 Arntz, Alexander;  Dia, Agostino Di;  Riebner, Tim;  Eimler, Sabrina C.
Machine Learning Concepts for Dual-Arm Robots within Virtual Reality Proceedings Article 
In: 2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), S. 168-172, 2021.
Links | BibTeX | Schlagwörter: 
@inproceedings{9644376,

title = {Machine Learning Concepts for Dual-Arm Robots within Virtual Reality},

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

doi = {10.1109/AIVR52153.2021.00038},

year  = {2021},

date = {2021-01-01},

booktitle = {2021 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)},

pages = {168-172},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.1109/AIVR52153.2021.00038
Schließen
733.
 Arntz, Alexander;  Kessler, Dustin;  Eimler, Sabrina C.
EnLighten: A Photovoltaics Learning Environment in Virtual Reality Proceedings Article 
In: 2021 International Conference on Advanced Learning Technologies (ICALT), S. 221-223, 2021.
Links | BibTeX | Schlagwörter: 
@inproceedings{9499737,

title = {EnLighten: A Photovoltaics Learning Environment in Virtual Reality},

author = {Alexander Arntz and Dustin Kessler and Sabrina C. Eimler},

doi = {10.1109/ICALT52272.2021.00072},

year  = {2021},

date = {2021-01-01},

booktitle = {2021 International Conference on Advanced Learning Technologies (ICALT)},

pages = {221-223},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
doi:10.1109/ICALT52272.2021.00072
Schließen
732.
 Arntz, Alexander;  Eimler, Sabrina C.;  Straßmann, Carolin;  Hoppe, Heinz Ulrich
On the Influence of Autonomy and Transparency on Blame and Credit in Flawed Human-Robot Collaboration Proceedings Article 
In: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, S. 377–381, Association for Computing Machinery, Boulder, CO, USA, 2021, ISBN: 9781450382908.
Abstract | Links | BibTeX | Schlagwörter: attribution of blame, human-robot collaboration, online study, perception of intelligence
@inproceedings{10.1145/3434074.3447196,

title = {On the Influence of Autonomy and Transparency on Blame and Credit in Flawed Human-Robot Collaboration},

author = {Alexander Arntz and Sabrina C. Eimler and Carolin Straßmann and Heinz Ulrich Hoppe},

url = {https://doi.org/10.1145/3434074.3447196},

doi = {10.1145/3434074.3447196},

isbn = {9781450382908},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction},

pages = {377–381},

publisher = {Association for Computing Machinery},

address = {Boulder, CO, USA},

series = {HRI '21 Companion},

abstract = {The collaboration between humans and autonomous AI-driven robots in industrial contexts is a promising vision that will have an impact on the sociotechnical system. Taking research from the field of human teamwork as guiding principles as well as results from human robot collaboration studies this study addresses open questions regarding the design and impact of communicative transparency and behavioral autonomy in a human robot collaboration. In an experimental approach, we tested whether an AI-narrative and communication panels of a robot-arm trigger the attribution of more human like traits and expectations going along with a changed attribution of blame and failure in a flawed collaboration.},

keywords = {attribution of blame, human-robot collaboration, online study, perception of intelligence},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
The collaboration between humans and autonomous AI-driven robots in industrial contexts is a promising vision that will have an impact on the sociotechnical system. Taking research from the field of human teamwork as guiding principles as well as results from human robot collaboration studies this study addresses open questions regarding the design and impact of communicative transparency and behavioral autonomy in a human robot collaboration. In an experimental approach, we tested whether an AI-narrative and communication panels of a robot-arm trigger the attribution of more human like traits and expectations going along with a changed attribution of blame and failure in a flawed collaboration.
Schließen
https://doi.org/10.1145/3434074.3447196
doi:10.1145/3434074.3447196
Schließen
731.
 Arntz, Alexander;  Eimler, Sabrina C.;  Hoppe, Heinz Ulrich
A Virtual Sandbox Approach to Studying the Effect of Augmented Communication on Human-Robot Collaboration Artikel 
In: Frontiers in Robotics and AI, Bd. 8, 2021, ISSN: 2296-9144.
Abstract | Links | BibTeX | Schlagwörter: 
@article{10.3389/frobt.2021.728961,

title = {A Virtual Sandbox Approach to Studying the Effect of Augmented Communication on Human-Robot Collaboration},

author = {Alexander Arntz and Sabrina C. Eimler and Heinz Ulrich Hoppe},

url = {https://www.frontiersin.org/articles/10.3389/frobt.2021.728961},

doi = {10.3389/frobt.2021.728961},

issn = {2296-9144},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Frontiers in Robotics and AI},

volume = {8},

abstract = {Human-Robot Collaboration (HRC) has the potential for a paradigm shift in industrial production by complementing the strengths of industrial robots with human staff. However, exploring these scenarios in physical experimental settings is costly and difficult, e.g., due to safety considerations. We present a virtual reality application that allows the exploration of HRC work arrangements with autonomous robots and their effect on human behavior. Prior experimental studies conducted using this application demonstrated the benefits of augmenting an autonomous robot arm with communication channels on subjective aspects such as perceived stress. Motivated by current safety regulations that hinder HRC to expand its full potential, we explored the effects of the augmented communication on objective measures (collision rate and produced goods) within a virtual sandbox application. Explored through a safe and replicable setup, the goal was to determine whether communication channels that provide guidance and explanation on the robot can help mitigate safety hazards without interfering with the production effectiveness of both parties. This is based on the theoretical foundation that communication channels enable the robot to explain its action, helps the human collaboration partner to comprehend the current state of the shared task better, and react accordingly. Focused on the optimization of production output, reduced collision rate, and increased perception of safety, a between-subjects experimental study with two conditions (augmented communication vs non-augmented) was conducted. The results revealed a statistically significant difference in terms of production quantity output and collisions with the robot, favoring the augmented conditions. Additional statistically significant differences regarding self-reported perceived safety were found. The results of this study provide an entry point for future research regarding the augmentation of industrial robots with communication channels for safety purposes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Schließen
Human-Robot Collaboration (HRC) has the potential for a paradigm shift in industrial production by complementing the strengths of industrial robots with human staff. However, exploring these scenarios in physical experimental settings is costly and difficult, e.g., due to safety considerations. We present a virtual reality application that allows the exploration of HRC work arrangements with autonomous robots and their effect on human behavior. Prior experimental studies conducted using this application demonstrated the benefits of augmenting an autonomous robot arm with communication channels on subjective aspects such as perceived stress. Motivated by current safety regulations that hinder HRC to expand its full potential, we explored the effects of the augmented communication on objective measures (collision rate and produced goods) within a virtual sandbox application. Explored through a safe and replicable setup, the goal was to determine whether communication channels that provide guidance and explanation on the robot can help mitigate safety hazards without interfering with the production effectiveness of both parties. This is based on the theoretical foundation that communication channels enable the robot to explain its action, helps the human collaboration partner to comprehend the current state of the shared task better, and react accordingly. Focused on the optimization of production output, reduced collision rate, and increased perception of safety, a between-subjects experimental study with two conditions (augmented communication vs non-augmented) was conducted. The results revealed a statistically significant difference in terms of production quantity output and collisions with the robot, favoring the augmented conditions. Additional statistically significant differences regarding self-reported perceived safety were found. The results of this study provide an entry point for future research regarding the augmentation of industrial robots with communication channels for safety purposes.
Schließen
https://www.frontiersin.org/articles/10.3389/frobt.2021.728961
doi:10.3389/frobt.2021.728961
Schließen
730.
 Baker, Nermeen Abou;  Szabo-Müller, Paul;  Handmann, Uwe
Transfer learning-based method for automated e-waste recycling in smart cities Artikel 
In: EAI Endorsed Transactions on Smart Cities, Bd. 5, Nr. 16, S. 1-9, 2021.
Links | BibTeX | Schlagwörter: Artificial Intelligence, Automated E-Waste Recycling, Circular Economy, Smart Cities, transfer learning
@article{EAI-BakSzaHan2021,

title = {Transfer learning-based method for automated e-waste recycling in smart cities},

author = {Nermeen Abou Baker and Paul Szabo-Müller and Uwe Handmann},

url = {https://eudl.eu/pdf/10.4108/eai.16-4-2021.169337},

doi = {10.4108/eai.16-4-2021.169337},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {EAI Endorsed Transactions on Smart Cities},

volume = {5},

number = {16},

pages = {1-9},

publisher = {EAI},

keywords = {Artificial Intelligence, Automated E-Waste Recycling, Circular Economy, Smart Cities, transfer learning},

pubstate = {published},

tppubtype = {article}

}

Schließen
https://eudl.eu/pdf/10.4108/eai.16-4-2021.169337
doi:10.4108/eai.16-4-2021.169337
Schließen
729.
 Baker, Nermeen Abou;  Szabo-Müller, Paul;  Handmann, Uwe
A Feature-Fusion Transfer Learning Method as a Basis to Support Automated Smartphone Recycling in a Circular Smart City Konferenz 
Science and Technologies for Smart Cities - 6th EAI International Conference, SmartCity360, Virtual Event, December 2-4, 2020, Proceedings, Springer Nature, Cham, Switzerland, 2021, ISBN: 978-3-030-76063-2, (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, volume 372).
Links | BibTeX | Schlagwörter: 
@conference{Springer-BakSzaHan2021,

title = {A Feature-Fusion Transfer Learning Method as a Basis to Support Automated Smartphone Recycling in a Circular Smart City},

author = {Nermeen Abou Baker and Paul Szabo-Müller and Uwe Handmann},

url = {https://link.springer.com/chapter/10.1007/978-3-030-76063-2_29},

doi = {10.1007/978-3-030-76063-2},

isbn = {978-3-030-76063-2},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Science and Technologies for Smart Cities - 6th EAI International Conference, SmartCity360, Virtual Event, December 2-4, 2020, Proceedings},

pages = {422-441},

publisher = {Springer Nature},

address = {Cham, Switzerland},

note = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, volume 372},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Schließen
https://link.springer.com/chapter/10.1007/978-3-030-76063-2_29
doi:10.1007/978-3-030-76063-2
Schließen
728.
 Wafo, Franck;  Mabou, Ivan Cedric;  Heilmann, Dan;  Zengeler, Nico;  Handmann, Uwe
A Transfer Learning Evaluation of Deep Neural Networks Konferenz 
IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), 2021.
Links | BibTeX | Schlagwörter: 
@conference{ICIEA-WafHanEtAl2021,

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

author = {Franck Wafo and Ivan Cedric Mabou and Dan Heilmann and Nico Zengeler and Uwe Handmann},

url = {https://ieeexplore.ieee.org/abstract/document/9516253},

doi = {10.1109/ICIEA51954.2021.9516253},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {IEEE 16th Conference on Industrial Electronics and Applications (ICIEA)},

pages = {1411-1416},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

Schließen
https://ieeexplore.ieee.org/abstract/document/9516253
doi:10.1109/ICIEA51954.2021.9516253
Schließen
2020
727.
 Nuñez, Tania Roxana;  Radtke, Theda;  Eimler, Sabrina C.
A third-person perspective on phubbing: Observing smartphone-induced social exclusion generates negative affect, stress, and derogatory attitudes Artikel 
In: Cyberpsychology: Journal of Psychosocial Research on Cyberspace, Bd. 14, Nr. 3, 2020.
BibTeX | Schlagwörter: 
@article{Nunez2020,

title = {A third-person perspective on phubbing: Observing smartphone-induced social exclusion generates negative affect, stress, and derogatory attitudes},

author = {Tania Roxana Nuñez and Theda Radtke and Sabrina C. Eimler},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Cyberpsychology: Journal of Psychosocial Research on Cyberspace},

volume = {14},

number = {3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Schließen
726.
 Nurhas, Irawan;  Geisler, Stefan;  Ojala, Arto;  Pawlowski, Jan Martin
Towards a Wellbeing-driven System Design for Intergenerational Collaborative Innovation: A Literature Review Proceedings Article 
In: Proceedings of the Annual Hawaii International Conference on System Sciences, 2020.
BibTeX | Schlagwörter: 
@inproceedings{Nurhas2020,

title = {Towards a Wellbeing-driven System Design for Intergenerational Collaborative Innovation: A Literature Review},

author = {Irawan Nurhas and Stefan Geisler and Arto Ojala and Jan Martin Pawlowski},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the Annual Hawaii International Conference on System Sciences},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
825 Einträge « ‹ 2 von 17 › »