Extraction of actuator forces and displacements involved in human walking and running and estimation of time-series neural signals by inverse dynamics simulation

While conventional biped robots are arithmetically controlled by CPU and driven by servo motors, humans locomote by contraction of muscles that receive electrical signals from the spinal cord. For real-time control without numerical calculations, we proposed a method that analog electronic circuits mimic neural circuits and output electrical signals. Gait control of a musculoskeletal robot requires this circuit and muscle-mimicking actuators. In this paper, we extracted the muscle displacements and generated forces involved in human walking and running with inverse dynamic simulation. The generated force and electromyogram were compared, and the main moving muscles were selected. The neural signals input to the muscles were derived by dividing the displacement graph into 6 sections and classifying the muscle groups by focusing on the maximum contraction. Also, we compared the generated forces, displacements, and the neural signals with physiological findings and discussed the similarity between the living body and the musculoskeletal model.