Accurate recognition of lower limb ambulation mode based on surface electromyography and motion data using machine learning. (September 2020)

• Record Type: Journal Article
• Authors: Zhou, Bin; Wang, Hong; Hu, Fo; Feng, Naishi; Xi, Hailong; Zhang, Zhihan; Tang, Hao
• Is Part Of: Computer methods and programs in biomedicine. Volume 193(2020)
• Publication Date: 2020-09
• Subjects: Surface electromyography -- Inertial measurement units -- Ambulation mode recognition -- Machine learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105486
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The fusion of sEMG signals and IMU data could achieve better classification results. The features selected by the MRF based Fisher-Markov selector have significant statistical differences between the groups. The features selected by the MRF based Fisher-Markov selector could lead to a higher classification accuracy. The SVM classifier could classify the lower limb ambulation modes well. Abstract: Background and Objective The lower limb activity of recognition of the elderly, the weak, the disabled and the sick is an irreplaceable role in the caring of daily life. The main purpose of this study is to assess the feasibility of using the surface electromyography (sEMG) signal and inertial measurement units (IMUs) data to determine the optimal fusion features and classifier for the study of daily ambulation mode recognition. Methods We have carried out several steps of experiments to obtain and test the optimal combination of the sEMG data and the body motion data at the feature level and the most suitable machine learning classification algorithm. Firstly, the sEMG and IMUs signals of eighteen participants performing four different ambulatory activities have recorded using wearable sensors. Secondly, several features of the sEMG sensors and IMU data were extracted and tested by the Markov Random Field based Fisher–Markov feature selector. Finally, four ML classifiers with several feature combinations were estimated with sensitivity, precision and recognition accurate rate of ambulatory activity classification. Results The results of this work showed that all selected features were significantly statistical difference in four ambulation modes. The principal component analysis was used to reduce the dimension of selected sEMG features and IMU features to form a fusion feature input support vector machine classifier, which could predict ambulatory activities with good classification performance. Conclusions It is concluded that the results demonstrate the feasibility of the ML classification model, which could provide a more novel way to guarantee the recognition rate and effectiveness of monitor daily ambulatory activity.