Sensor-Agnostic, LSTM-Based Human Motion Prediction Using sEMG Data

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 h...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 25; no. 17; p. 5474
Main Authors:	Koo, Bon Ho, Siu, Ho Chit, Petersen, Lonnie G.
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 03.09.2025
Subjects:	Algorithms Classification Computational linguistics Data collection Data entry Deep Learning Electromyography Electromyography - methods Human subjects Humans Language processing LSTM Machine learning Motion motion prediction Movement - physiology Natural language interfaces Neural networks Neural Networks, Computer Sensors Signal processing surface electromyography United States Massachusetts United States > US deep learning motion prediction LSTM surface electromyography
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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