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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s25175474