Sensor-Agnostic, LSTM-Based Human Motion Prediction Using sEMG Data
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sensors-25-05474.pdf
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Author(s)
Koo, Bon Ho
Siu, Ho Chit
Petersen, Lonnie G.
Date Issued
September 2, 2025
Journal
Sensors
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Koo, B.H.; Siu, H.C.; Petersen, L.G. Sensor-Agnostic, LSTM-Based Human Motion Prediction Using sEMG Data. Sensors 2025, 25, 5474.
Version
Final published version
Abstract
The use of surface electromyography (sEMG) for conventional motion classification and prediction has had limitations due to sensor hardware differences. With the popularization of deep learning-based approaches to the application of motion prediction, this study explores the effects that different hardware sensor platforms have on the performance of a deep learning neural network trained to predict the one-degree-of-freedom (DoF) angular trajectory of a human. Two different sEMG sensor platforms were used to collect raw data from subjects conducting exercises, which was used to train a neural network designed to predict the future angular trajectory of the arm. The results show that the raw data originating from different sensor hardware with different configurations (including the communication method, data acquisition unit (DAQ) usage, electrode configuration, buffering method, preprocessing method, and experimental variables like the sampling frequency) produced bi-LSTM networks that performed similarly. This points to the hardware-agnostic nature of such deep learning networks.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Lincoln Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Institute for Medical Engineering and Science
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/s25175474
