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RISS 인기검색어
- 검색키워드
- 저자 : Jirou Feng (검색결과 3 건)
- 무료
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- 유료
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Hwayeong Jeong,Jirou Feng,Jung Kim 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10
Respiration gives important indicators for diagnosing the disease. At the same time, the strength of the respiration muscle can be directly related to the breathing ability of the patient, which decides the physical movement ability. Electromyography is the method that the respiration rate and the muscle function can be obtained at the same time. Therefore, the method of continuously capturing the EMG signal on the skin surface is a way to continuously monitoring information about the condition of patients without inconvenience for daily life. However, the current commercial surface electromyography (sEMG) sensors are bulky because of the separate gain amplifier, and they are made of a rigid material, which increases the physical contact resistance. Therefore, to make a wearable sEMG sensor that can be worn for a long time comfortably, it is necessary to have a structure that is small and changing depending on the shape of the body. This paper presents a flexible and stretchable fabric-based sEMG sensor with high SNR, and we apply proposing sensors to the respiration muscles to capture the indicators that can be used for evaluating the health state of the wearer.
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Seulki Kyeong,Jirou Feng,Jae Kwan Ryu,Jung Jae Park,Kyeong Ha Lee,Jung Kim 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.3
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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