Human motor function estimation based on EMG signal fractal dimension standard deviation

Issue title: Artificial Intelligence as a maturing and growing technology: An urgent need for intelligent systems

Guest editors: X. Yuan and M. Elhoseny

Article type: Research Article

Authors: Zhang, Xia^{a; *} | Tao, Sihan^a | Hu, Jinjia^a | Lin, Shuai^a | Hashimoto, Minoru^b

Affiliations: [a] Department of Mechatronics and Automobile Engineering, Chongqing Jiaotong University, Chongqing, P.R. China | [b] Robotics Institutes, Shinshu University, Ueda, Nagano, Japan

Correspondence: [*] Corresponding author. Xia Zhang, Department of Mechatronics and Automobile Engineering, Chongqing Jiaotong University, No. 66 Xuefudadao, Nanan District, Chongqing, P.R. China. E-mail: zx512@126.com.

Abstract: Wearable robots must adjust the assist mode/intensity according to human motion during the motion assistance process. By decoding the surface electromyography (sEMG) signal, the standard deviation of the fractal dimension is used as a characteristic index of muscle contraction-relaxation ability, and explore the feasibility of using the standard deviation of the fractal dimension to estimate the human motor function and thus provide a basis for decision-making for the flexible control of wearable robots. First, the sEMG signals of several subjects with different motor functions were collected and their time-domain and frequency-domain features were extracted. The experimental results for one hour of walking showed that the time-domain and frequency-domain feature values increased with muscle fatigue. The trend has little to do with the inherent motor function of the human body; Second, due to the strong nonlinearity, time-varying, and strong complexity of the sEMG signal, the fractal dimension nonlinear method is used to characterize the complexity of the EMG signal that is closely related to muscle function. Besides, theoretical and experimental studies have been conducted to clarify the feasibility of the complexity of fractal dimension representation and to provide theoretical support for the further use of the standard deviation of fractal dimension to estimate human motor function. The experimental results of continuous walking for one hour show that, macroscopically, the fractal dimension of each muscle of the individual subject does not change significantly with walking time, which shows that the fractal dimension has nothing to do with exercise time and muscle fatigue; On the microscopic level, the value of the fractal dimension changes when the subject’s muscles contract and relax. Subjects with strong motor function have smaller fractal dimensions when their muscles contract than subjects with weaker motor function, and the opposite happens when their muscles relax, and it can be seen that there is a positive correlation between the difference in the fractal dimension during muscle contraction and relaxation and the muscle contraction-relaxation ability and the human body’s inherent motor function. The test results verify the feasibility of using the standard deviation of fractal dimension to estimate the intrinsic motor function of the human body.

Keywords: EMG signal, human-robot interaction, standard deviation of fractal dimension, human motor function

DOI: 10.3233/JIFS-189358

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 3193-3205, 2021