Nils Grimmelsmann
Papers
2
Total Citations
19
H-Index
2
About
Nils Grimmelsmann is a researcher advancing the field of human-robot interaction through biosignal-based control systems. His primary research areas include surface electromyography (sEMG) signal processing, biomechanical modeling, and the prediction of human limb movements for wearable robotic applications. Grimmelsmann’s major contribution lies in developing methods that enable intuitive, non-invasive control of assistive devices such as active orthoses and exoskeletons. His most-cited work (2023, 14 citations) introduces a novel approach that combines sEMG signals with a biomechanical model based on individual anatomical and physiological measures, using a reduced set of optimization parameters to predict forearm movements. This work directly addresses the challenge of creating user-friendly interfaces for untrained operators in healthcare and rehabilitation settings. In a related study (2022, 5 citations), Grimmelsmann systematically evaluated sEMG signal features and segmentation parameters for limb movement prediction using feedforward neural networks, demonstrating that sEMG signals contain early information about movement onset and completion. His research is notable for bridging the gap between complex biomechanical modeling and practical, real-time control systems, offering promising pathways toward more responsive and intuitive wearable robotic technologies.
Research Focus
Key Achievements
Top Papers
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