Nihon Kikai Gakkai ronbunshu (Oct 2024)
Effect of impedance characteristics on human cortical and upper limb muscle activity and work characteristics in human–robot physical interaction
Abstract
Impedance control has been extensively used in robot control in physical human–robot interactions between humans and robots. To achieve smooth operation, various evaluations of operator responses have been performed during robot operation under impedance control. Although conventional studies have extensively employed assessments of motion characteristics based on manipulation force and hand tip positions and subjective evaluations, limited research has been conducted on physiological indicator evaluations to objectively assess human sensibility. During robot operation, humans plan control strategies for bodily movements by perceiving impedance parameters in the brain. Consequently, brain activity measurements have been extensively used to investigate the impact of impedance parameters on operators. In addition, recent research has elucidated control strategies based on human hand tip motion based on motor characteristics and muscle activity. However, conventional research has seen limited efforts in elucidating control strategies based on human hand tip motion, and simultaneous measurement and evaluation of motor characteristics and brain/muscle activity are seldom performed. Therefore, in this experiment, the forces, hand tip positions, muscle activity, and brain activity of the operators were measured during physical interactions, and statistical evaluations were performed on the operator’s physiological responses and motor characteristics arising from differences in impedance parameters. The experiment results indicate that impedance parameters influence motor characteristics and brain/muscle activity. Furthermore, the brain and muscle activity measurements indicate a correlation between brain activation and muscle force output, suggesting that insights into control strategies based on human manipulative forces and hand tip motion can be partially elucidated through temporal data analysis of hand velocity.
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