Data-Driven Human-Robot Coordination Based Walking State Monitoring With Cane-Type Robot

The walking state monitoring is indispensable during the robot-aided walking of people with lower limb dysfunctions. In this paper, the existence of human–robot coordination state is first statistically verified in the process of using a walking-aid cane-type robot during walking. Based on this coordination, a new walking state monitoring method is proposed by using the principal component analysis (PCA). The abnormal or emergency walking state is promptly detected if the new sample data are found to deviate from an off-line PCA model, which is generated from plentiful normal walking data of different subjects. Furthermore, a state diagnosis algorithm based on the contribution plot is also developed for the walking state recognition and diagnosis. In this way, typical abnormal states like the leg restrictions can be distinguished from the emergency states including falls and the stumbling. Moreover, the human–robot coordination analysis can be performed using less sensors built-in the robot without needing the posture information of full human body. The effectiveness of the proposed method is proven by experiments. Better recognition rate and real-time performance of the method are also verified by comparing with conventional center of pressure based monitoring method.