The recognition of grasping force using LDA. (January 2019)

• Record Type: Journal Article
• Authors: Wang, Nianfeng; Lao, Kunyi; Zhang, Xinhao; Lin, Jinfan; Zhang, Xianmin
• Is Part Of: Biomedical signal processing and control. Volume 47(2019)
• Page Start: 393
• Page End: 400
• Publication Date: 2019-01
• Subjects: Electromyography -- Myoelectric control -- Grasping force -- LDA -- Quadratic polynomial fitting; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.06.011
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Pattern recognition is utilized to recognize grasping force. The optimal feature set includes the MAV, RMS and WL. The six-dimensional vector matrix of the optimal feature set is reduced by LDA. Quadratic polynomial fitting is used to get continuous values of grasping force. Abstract: This paper proposes an EMG recognition system of grasping force on the basis of the pattern recognition, which can classify the surface electromyography (sEMG) signals from 2 electrodes and recognize the grasping force. Ten characteristics in time domain and frequency domain are chosen as the primary features to combine feature sets, to obtain an optimal feature set. The linear discriminant analysis (LDA) is used to reduce the dimension of the features vector to a one-dimensional vector matrix, and pattern recognition to classify and recognize it. In online recognition, to obtain continuous recognition values, the quadratic polynomial fitting is utilized to find the relationship between the one-dimensional vector matrix and grasping forces.