sEMG 분석을 이용한 높이 들어올리기 자세에서의 신체적 작업부하의 정량적 평가방법 개발
Evaluation Method of Physical Workload in Overhead Lifting Posture Using Surface EMG Analysis
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For human factor engineering and wearable robot design, the quantitative assessment of physical workload is needed. Through measuring the surface EMG (sEMG) and analysis, the physical workload in overhead lifting posture is presented in quantitative manner. By normalizing sEMG activities with maximal voluntary contraction (MVC),the inter-subject variability is reduced. In all muscles, %MVC increased as the weight of lifting object increases. In anterior deltoid muscle, the %MVC was 3-4 times higher than the other muscles which imply that this muscle performs the major role in the overhead lifting posture. In fatigue analysis, %MVC and the mean frequency in muscle of anterior deltoid changed markedly when compared with other muscles. Through the suggested procedures and analysis, the physical workload for a specific posture can be represented in quantitative way but the clinical meaning for the value should be investigated further.
Abstract
For human factor engineering and wearable robot design, the quantitative assessment of physical workload is needed. Through measuring the surface EMG (sEMG) and analysis, the physical workload in overhead lifting posture is presented in quantitative manner. By normalizing sEMG activities with maximal voluntary contraction (MVC),the inter-subject variability is reduced. In all muscles, %MVC increased as the weight of lifting object increases. In anterior deltoid muscle, the %MVC was 3-4 times higher than the other muscles which imply that this muscle performs the major role in the overhead lifting posture. In fatigue analysis, %MVC and the mean frequency in muscle of anterior deltoid changed markedly when compared with other muscles. Through the suggested procedures and analysis, the physical workload for a specific posture can be represented in quantitative way but the clinical meaning for the value should be investigated further.
참고문헌 (18)
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