49 citations · 131 across the 37 of their papers we have counts for
43 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…
Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation
Tim Sziburis, Susanne Blex, Tobias Glasmachers +1
The identification of individual movement characteristics sets the foundation for the assessment of personal rehabilitation progress and can provide diagnostic information on level…
GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals
Omair Ali, Muhammad Saif-ur-Rehman, Marita Metzler +3
Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) techno…
Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
Abhijeet Pendyala, Asma Atamna, Tobias Glasmachers
We present a proximal policy optimization (PPO) agent trained through curriculum learning (CL) principles and meticulous reward engineering to optimize a real-world high-throughput…
Ruhr Hand Motion Catalog of Human Center-Out Transport Trajectories in 3D Task-Space Captured by a Redundant Measurement System
Tim Sziburis, Susanne Blex, Tobias Glasmachers +1
Neurological conditions are a major source of movement disorders. Motion modelling and variability analysis have the potential to identify pathology but require profound data. We i…
Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation
Simon Hakenes, Tobias Glasmachers
This work addresses the challenge of navigating expansive spaces with sparse rewards through Reinforcement Learning (RL). Using topological maps, we elevate elementary actions to o…