Veröffentlichungen

@article{schmidtConceptsMuscleActivity2023,

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

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

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

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

issn = {1475-925X},

year  = {2023},

date = {2023-06-24},

urldate = {2023-06-24},

journal = {BioMedical Engineering OnLine},

volume = {22},

number = {1},

pages = {63},

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

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

pubstate = {published},

tppubtype = {article}

}

@article{lehmlerTransferLearningPatientSpecific2021bb,

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

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

editor = {{IOP Publishing},

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

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

year  = {2022},

date = {2022-10-28},

urldate = {2022-10-28},

journal = {Journal of Neural Engineering},

volume = {19},

number = {5},

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

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

pubstate = {published},

tppubtype = {article}

}

@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}

}