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Surface Electromyography Characteristics for Motion Intention Recognition and Implementation Issues in Lower-limb Exoskeletons

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  • Intelligent Control and Applications
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Abstract

Recognizing the user’s motion intentions is a crucial challenge to develop human augmented robotic devices due to safety and easiness of interactions. Among the possible sensorial modalities, surface electromyography (sEMG) signals have been tested to be a primary motion intention channel due to the inherent advantage of electromechanical delay and the muscle activation information. However, the lack of detailed sEMG characteristics as motion recognition has been difficult issues to develop safe and intuitive interactions with the robots. In this study, we evaluated the sEMG characteristics for their potential applicability to recognizing the motion intentions of humans. For the discrete motion intention recognition, the walking environments were classified using only sEMG signals by support vector machine (SVM) and linear discriminated analysis (LDA) models with accuracy of 79.1% and 76.3%. Due to the fact that it is crucial to identify an unexpected disturbance by the collision between the exoskeleton and surrounding environment in recognizing the user intention to guarantee the safety of the user, sEMG and torque sensors were used to classify user-intended interaction forces and disturbance forces in the event of collisions. A control algorithm was proposed that detects and compensates for collisions, and its performance showed that robust motion intention recognition and control of powered exoskeletons are possible. We investigated the effect of muscle fatigue caused by long-term walking with heavy load wearing an exoskeleton. The sEMG amplitude and frequency were analyzed for muscle fatigue due to single-joint (knee extensions) and multi-joint (walking) exercises, and muscle fatigue due to walking was prominent in the signal from the vastus medialis (VM). The characteristics of sEMG due to muscle fatigue should be seriously considered in continuous motion estimation.

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Funding

This research was supported by LIG Nex1.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Korea

    Seulki Kyeong, Jirou Feng & Jung Kim

  2. LIG Nex 1, 333, Pangyo-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea

    Jae Kwan Ryu, Jung Jae Park & Kyeong Ha Lee

Authors
  1. Seulki Kyeong
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  2. Jirou Feng
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Corresponding author

Correspondence to Jung Kim.

Additional information

Seulki Kyeong received her B.S., M.S. and Ph.D. degrees in mechanical engineering in 2014, 2016 and 2022, from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, respectively. Her research interests include wearable robotics and surface electromyography (sEMG)-based physical human-robot interactions.

Jirou Feng received her B.S. degree in Electrical Engineering from Harbin Institute of Technology, Harbin, China, in 2017, and her M.S. degree from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 2019. She is currently pursuing a Ph.D. degree at KAIST. Her research interests include surface electromyography (sEMG) signal processing, human-machine interface and wearable robotics.

Jae Kwan Ryu received his M.E. degree in mechanical engineering, Kyung Hee University in Korea. He acquired his Ph.D. in robotics in 2009 from Japan Advanced Institute of Science and Technology (JAIST). He is now a senior engineer at Robotic/Unmanned Systems Lab. of LIG Nex1. His main research interests are powered exoskeleton systems, hydraulic systems, manipulators, machine learning, biological mimetics, military unmanned systems (UGV, USV).

Jung Jae Park received his B.S. and M.S. degrees from the Department of Mechanical and Aerospace Engineering at Seoul National University, Seoul, Korea, in 2017 and 2019, respectively. He is currently a research engineer with the Unmanned/Robotics Team of LIG Nex1. His research interests include nano/micro fabrication process development for flexible, stretchable and wearable electronics, human-machine interface, robotics, and artificial intelligence.

Kyeong Ha Lee received his B.S. and Ph.D. degrees in mechanical engineering from Sungkyunkwan University, Suwon, Korea, in 2014 and 2020, respectively. He is currently a research engineer with the Unmanned/Robotics Team of LIG Nex1. His research interests include control of electro-hydraulic systems, robotics, human-robot interaction, and artificial intelligence.

Jung Kim received his B.S. and M.S. degrees in mechanical engineering from the Department of Mechanical Engineering, Korea Advanced Institute of Science and Technologies (KAIST), Korea, in 1991 and 1993, respectively, and a Ph.D. degree in mechanical engineering from the Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 2003. He is currently a Professor in the Department of Mechanical Engineering, KAIST. His current research interests include medical robotics, haptics, biomechanical signals, and assistive robotics.

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Kyeong, S., Feng, J., Ryu, J.K. et al. Surface Electromyography Characteristics for Motion Intention Recognition and Implementation Issues in Lower-limb Exoskeletons. Int. J. Control Autom. Syst. 20, 1018–1028 (2022). https://doi.org/10.1007/s12555-020-0934-3

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