Our paper Koopman-driven Grip Force Prediction through EMG Sensing is published in IEEE TNSRE!

Our paper Koopman-driven Grip Force Prediction through EMG Sensing, has been published in IEEE Transactions on Neural Systems and Rehabilitation Engineering!

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The research is authored by Tomislav Bazina, Ervin Kamenar, Maria Fonoberova, and Igor Mezić in collaboration with AIMdyn, Inc.

The study focuses on estimating and predicting hand grip force using a single pair of surface electromyography (EMG) sensors. By applying novel signal processing techniques and a Koopman-based data-driven approach, the research achieved accurate real-time force estimations and predictions with minimal error. A static Koopman operator is used to estimate grip force from EMG measurements, while a dynamic Koopman operator predicts the future (up to 0.5 s ahead) state of the grip force from the current estimate. The whole framework is real-time ready and will be used in rehabilitation robotics applications.

This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-22-1-0531 and the University of Rijeka (Grant UNIRI-ISKUSNI-23-47). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force.