Joint Torque Estimation Using sEMG and Deep Neural Network

• Published in: Journal of electrical engineering & technology Vol. 15; no. 5; pp. 2287 - 2298
• Main Authors: Kim, Harin, Park, Hyeonjun, Lee, Sangheum, Kim, Donghan
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.09.2020; 대한전기학회
• Subjects: Electrical Engineering; Electrical Machines and Networks; Electronics and Microelectronics; Engineering; Instrumentation; Original Article; Power Electronics; 전기공학; Deep neural network; Multi channels surface-Electromyography (sEMG); Electromyogram; Regression; Upper limb; PRTE system; Joint torque estimation
• ISSN: 1975-0102; 2093-7423
• DOI: 10.1007/s42835-020-00475-w

With the aid of various physical and biological sensors, research is actively being conducted to understand the intention of wearer’s motions through parameters such as joint torque. sEMG signals can be measured faster than physical sensors, which are often used in the field of behavioral intent identification studies. However, electrodes must be placed in the correct positions, and due to the high volume of noise, professional knowledge and accurate hardware design are required. In this paper, a system is constructed to improve the sEMG signal measurement environment by producing small multichannel sEMG modules. In addition, deep neural network supervised learning algorithms are implemented to estimate the torque using only the sEMG signal. Based on this, we analyze the organization of algorithms, the processing of the sEMG data, and how the number of channels affects learning. The optimal deep natural network model selected by the analysis is implanted to embedded after learning. The implanted model performs a portable real-time torque optimization (PRTE) according to the sEMG signal entered. In this paper, we study the deep natural network algorithm for estimating sEMG hardware and torque, and how it is implanted into a portable embedded system for use in estimating real-time motion intent. The proposed deep natural network algorithm and the usefulness of the PRTE system are verified through experiments.