• Journal of the Korean Society for Precision Engineering
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• v.28 no.6
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• pp.663-669
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• 2011

This paper examines methods to estimate torque and angle in shoulder flexion from surface electromyography(sEMG) signals for intuitive and delicate control of robotic assistance device. Five muscles on the upper arm, three for shoulder flexion and two for shoulder extension, were used to offer favorable sEMG recording conditions in the estimation. The methods tested were the mean absolute value (MAV) with linear regression and the artificial neural network (ANN) method. An optimal condition was sought by varying combination of muscles used and the parameters in each method. The estimation performance was evaluated using the correlation values and normalized root mean square error values. In addition, we discussed their possible use as an estimation of motion intent of a user or as a command input in a physical human-machine interaction system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.11
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• pp.1391-1398
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• 2011

Biomechanical analysis of arm motion during steering was performed using a motion analysis technique. Three-dimensional position data for each part of arm are fed into an interactive model combining a musculoskeletal arm model and the mechanical steering system to calculate joint angles and torques using inverse kinematic and dynamic analyses, respectively. The analysis shows that elbow pronation/supination, wrist flexion/extension, shoulder adduction/abduction, and shoulder flexion/extension have significant magnitudes. Sensitivity analysis of the arm joint motion with respect to seating posture and steering wheel configuration is carried out to investigate the qualitative influence of the seating posture and driver's seat configuration on the steering behavior.