Hardware CPG Model That Generates Time Series Neural Signal Patterns for Controlling Human Walking and Running

Abstract

Abstract A hardware CPG model for biped gait control using pulse-type hardware neural networks(P-HNNs) composed of analog electronic circuits to generate time-series neural signal patterns for controlling human walking and running is reported. Human walking and running are controlled by central pattern generators located in a spinal cord. Apart from this spinal system, gait and gait speed is known to be changed by a signal from a higher center input to a brainstem-spinal cord projection. However, the specific circuit configuration of the neural circuits for controlling gait locomotion is still unknown. In recent studies, walking and running have been estimated to be controlled with five simple time-series pulse patterns generated by the spinal cord and the duration of their pulses. We have previously used P-HNNs to generate time-series neural signal pulse patterns for controlling human walking and running. In the model, the order of the time series was determined manually by trigger input. In addition, the change in the pulse pattern period when switching from walking to running was not reproduced. In this paper, we propose a hardware CPG model for biped gait control, with a network configuration capable of automatically determining the order of the time series, and changing the pulse pattern period. Based on the circuit configuration of the proposed CPG model, circuit simulations and CPG circuits fabricated with surface mount components were measured. The measured results confirm the automatic output of the neural signal patterns for controlling walking and running in time-series order. We also confirmed the pulse pattern period becomes shorter when switching from walking to running.