Abstract
Gestures recognition based on surface electromyography (sEMG) has been widely used for human-computer interaction. However, there are few research studies on overcoming the influence of physiological factors among different individuals. In this paper, a cross-individual gesture recognition method based on long short-term memory (LSTM) networks is proposed, named cross-individual LSTM (CI-LSTM). CI-LSTM has a dual-network structure, including a gesture recognition module and an individual recognition module. By designing the loss function, the individual information recognition module assists the gesture recognition module to train, which tends to orthogonalize the gesture features and individual features to minimize the impact of individual information differences on gesture recognition. Through cross-individual gesture recognition experiments, it is verified that compared with other selected algorithm models, the recognition accuracy obtained by using the CI-LSTM model can be improved by an average of 9.15%. Compared with other models, CI-LSTM can overcome the influence of individual characteristics and complete the task of cross-individual hand gestures recognition. Based on the proposed model, online control of the prosthetic hand is realized.
Highlights
Introduction e core task ofsurface electromyography (sEMG)-based prosthetic hand control and human-computer interaction applications is to decode human motion intentions through sEMG signals [1]
By designing the loss function, the individual information recognition module assists the gesture recognition module to train, which tends to orthogonalize the gesture features and individual features to minimize the impact of individual information differences on gesture recognition. rough cross-individual gesture recognition experiments, it is verified that compared with other selected algorithm models, the recognition accuracy obtained by using the CI-long short-term memory (LSTM) model can be improved by an average of 9.15%
The original sEMG signal collected from 4 subjects is preprocessed by the bandpass filter of 10 Hz–300 Hz and the notch filter of 50 Hz, and the active segment of the signal is extracted according to 30% of the maximum amplitude of each data as the threshold
Summary
Introduction e core task ofsEMG-based prosthetic hand control and human-computer interaction applications is to decode human motion intentions through sEMG signals [1]. One is the research on identifying discrete motion states of limbs through sEMG signals, such as independent motion states like flexion and extension of fingers, making fists, and turning wrists [3,4,5]. Is paper only conducts research on the recognition of discrete movements of limbs through sEMG signals. Discrete action pattern classification is currently the most mature and fruitful method in the field of human action recognition based on sEMG. Representative research work includes the following: Englehart et al [7] compared the effects of different features of sEMG on the accuracy of gesture classification and used linear discriminant analysis (LDA) for the first time to perform action recognition on the time-frequency domain features of sEMG, which can accurately identify 6 types of hand/wrist movements.
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