Hand medical monitoring system based on machine learning and optimal EMG feature set

• Published in: Personal and ubiquitous computing Vol. 27; no. 6; pp. 1991 - 2007
• Main Authors: Yu, Mingchao, Li, Gongfa, Jiang, Du, Jiang, Guozhang, Tao, Bo, Chen, Disi
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Back propagation networks; Bioelectricity; Classifiers; Computer Science; Discriminant analysis; Electromyography; Feature recognition; Feature selection; Gesture recognition; Hand (anatomy); Inspection; Machine learning; Medical personnel; Mobile Computing; Monitoring systems; Motion perception; Neural networks; Original Article; Personal Computing; Rehabilitation; Signal monitoring; Signal processing; Support vector machines; Telemedicine; User Interfaces and Human Computer Interaction; EMG signal; Medical monitoring; Feature extraction; EMG feature set; Machine learning; Gesture recognition
• ISSN: 1617-4909; 1617-4917
• DOI: 10.1007/s00779-019-01285-2

Considering that serious hand function damage will greatly affect the daily life of patients, its recovery mainly depends on the regular inspection and manual training of medical staff, and medical monitoring based on bioelectric signals can largely replace manual re-examination as autonomous rehabilitation technology. So, for the rationality of feature selection and the diversity of classifier design in the gesture recognition process based on electromyography (EMG) signals, this paper proposes a hand medical monitoring system based on feature selection method of feature subset average recognition rate and optimal machine learning algorithm selection, which mainly depends on the prediction of hand movement. At the same time, since most experiments are conducted in different non-public proprietary databases, the comparison between various gesture recognition methods can only be analyzed to a certain extent. Therefore, this paper uses the DB1 dataset in the large publicly available NinaPro database and combines with presently well-known 11 time-domain (TD) features and 5 frequency domain (FD) features, then uses the support vector machine (SVM) classifier to comparative analysis total 136 feature combinations under various feature numbers. Under the premise of ensuring the overall recognition rate of electromyography gesture, this method will be able to reduce the number of features in feature set, according to the change of the average remove redundant features, and construct an optimal reduced EMG feature set. Finally, through the four common hand motion classifiers based on machine learning: SVM, back propagation neural network, linear discriminant analysis, and K-nearest neighbor, this paper tests and verifies the separability of the optimal reduced EMG feature set, and based on this, selects the optimal hand motion classifier to build the optimal hand motion recognition system, improve the hand medical monitoring system, and provide technical reference for the construction of real-time medical monitoring system.