A CNN-LSTM model for six human ankle movements classification on different loads

This study aims to address three problems in current studies in decoding the ankle movement intention for robot-assisted bilateral rehabilitation using surface electromyogram (sEMG) signals: (1) only up to four ankle movements could be identified while six ankle movements should be classified to pro...

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Bibliographic Details
Published in:Frontiers in human neuroscience 2023-03, Vol.17, p.1101938
Main Authors: Li, Min, Wang, Jiale, Yang, Shiqi, Xie, Jun, Xu, Guanghua, Luo, Shan
Format: Article
Language:English
Subjects:
Accuracy
Ankle
ankle movement classification
Classification
CNN
Computer applications
Deep learning
Electromyography
Gait
Human mechanics
Human Neuroscience
load variation
Long short-term memory
LSTM
Neural networks
Older people
Rehabilitation
Robots
SEMG signal
Stroke
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Summary:This study aims to address three problems in current studies in decoding the ankle movement intention for robot-assisted bilateral rehabilitation using surface electromyogram (sEMG) signals: (1) only up to four ankle movements could be identified while six ankle movements should be classified to provide better training; (2) feeding the raw sEMG signals directly into the neural network leads to high computational cost; and (3) load variation has large influence on classification accuracy. To achieve this, a convolutional neural network (CNN)-long short-term memory (LSTM) model, a time-domain feature selection method of the sEMG, and a two-step method are proposed. For the first time, the Boruta algorithm is used to select time-domain features of sEMG. The selected features, rather than raw sEMG signals are fed into the CNN-LSTM model. Hence, the number of model's parameters is reduced from 331,938 to 155,042, by half. Experiments are conducted to validate the proposed method. The results show that our method could classify six ankle movements with relatively good accuracy (95.73%). The accuracy of CNN-LSTM, CNN, and LSTM models with sEMG features as input are all higher than that of corresponding models with raw sEMG as input. The overall accuracy is improved from 73.23% to 93.50% using our two-step method for identifying the ankle movements with different loads. Our proposed CNN-LSTM model have the highest accuracy for ankle movements classification compared with CNN, LSTM, and Support Vector Machine (SVM).
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2023.1101938