Classification and simulation of process of linear change for grip force at different grip speeds by using supervised learning based on sEMG. (15th November 2022)

• Record Type: Journal Article
• Authors: Wu, Yansheng; Liang, Shili; Yan, Tao; Ao, Jinxin; Zhou, Ze; Li, Xiuping
• Is Part Of: Expert systems with applications. Volume 206(2022)
• Publication Date: 2022-11-15
• Subjects: Surface electromyography (sEMG) -- Grip speed -- Grip force -- Simulate -- Adaptive filter -- Classification; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117785
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The process of linear change for grip force can be accurately simulated by sEMG. The different grip patterns can be accurately classified by sEMG. The NLMS overcomes the defects of traditional denoising method. Abstract: Bionic prosthesis can help the disabled rebuild the athletic ability of their missing limbs. Biomimetic hand controlled by surface electromyography (sEMG) has been widely studied in recent years. However, a lot of researchers focus on the field of movement recognition, ignoring the precise control of grip speed and force in the design of the prosthesis. The study presents a new means to simulate the process of linear changes of grip force by using sEMG, which provides a simple and practicable way of precise control of the speed and strength of force for the myoelectric hand. We design a synchronous acquisition device for sEMG signal and grip force signal. A novel preprocessing methodology for the raw sEMG signal is introduced, which is adaptive filter based on the Normalized Least Mean Square (NLMS). Compared the traditional preprocessing method that uses a notch filter for removing power line interference (50 Hz), the presented method can effectively remove power line interference and its harmonic wave components, and it completely reserves useful information contained in the sEMG signal. The linear change process of grip force at five grip speeds are studied. A simple and efficient method for muscle contraction state detection is used. Six supervised classification algorithms are used to identify these grip patterns and achieve a highest recognition rate of over 99%. Five supervised regression algorithms are applied to predict the value of grip force and establish the mapping relationship between sEMG and force. The experimental results demonstate that the method can simulate the linear change process of grip force well, and it achieves a minimum standard deviation of 0.14272.