Related Experiment Video
Updated: Jul 16, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
Published on: March 28, 2025
On the Distribution of Muscle Signals: A Method for Distance-Based Classification of Human Gestures
Jonas Große Sundrup1, Katja Mombaur1,2
1Canada Excellence Research Chair Human-Centred Robotics and Machine Intelligence, Systems Design Engineering & Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Abstract:
We investigate the distribution of muscle signatures of human hand gestures under Dynamic Time Warping. For this we present a k-Nearest-Neighbors classifier using Dynamic Time Warping for the distance estimate. To understand the resulting classification performance, we investigate the distribution of the recorded samples and derive a method of assessing the separability of a set of gestures. In addition to this, we present and evaluate two approaches with reduced real-time computational cost with regards to their effectiveness and the mechanics behind them. We further investigate the impact of different parameters with regards to practical usability and background rejection, allowing fine-tuning of the induced classification procedure.
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