Posture Estimation by Clustering Pressure Information and Control Implementation for Pneumatically Driven Gait-Assistive Robot

Pneumatic artificial muscles (PAMs) are light and soft, and are expected to be applied to gait assistance robots with multiple actuators on the human body. The PAMs can be used as not only actuators, but also sensors to detect the gait phase by using their deformable bodies whose internal pressure changes in response to the wearer’s gait. However, conventional methods to detect the gait phase by PAMs have targeted single point detection in a gait phase and used for only ON/OFF control of the PAM actuators. In this study, we proposed an algorithm to estimate the postural state of the wearer, especially the state of both hip joints, from the internal pressure information of the PAMs with a small amount of calculation by using clustering method, and succeeded in controlling the PAMs’ pressure continuously based on this algorithm. The effectiveness of the proposed control method was verified through gait-assistive experiments using a treadmill. We measured the electromyogram of the adductor longus muscle under 3 subjects and a one-sided significant difference test was performed. As a result, we confirmed significant differences at the 1% significance level for 2 subjects and at the 10% significance level for the remaining subject, allowing us to evaluate the effectiveness of the proposed PAM control strategy.