3 citations · 3 across the 3 of their papers we have counts for
3 papers
Applicability of memorization indicators for early spotting of overfitting while recalibrating sEMG-decoders on low sample sizes
Stephan J. Lehmler, Tobias Glasmachers, Ioannis Iossifidis
Deep learning models for surface electromyography (sEMG) can benefit substantially from subject-specific (re-)calibration, since no sufficiently large and diverse datasets are avai…
Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes
Stephan Johann Lehmler, Muhammad Saif-ur-Rehman, Tobias Glasmachers +1
To gain a deeper understanding of the behavior and learning dynamics of (deep) artificial neural networks, it is valuable to employ mathematical abstractions and models. These tool…
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification
Stephan Johann Lehmler, Muhammad Saif-ur-Rehman, Tobias Glasmachers +1
Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-machine-interfaces (MMI) and their application for e.g. rehabilitation therapy. sEMG signals have high…