Human Gait-Phase Classification to Control a Lower Extremity Exoskeleton Robot

A lower extremity exoskeleton is a robot device that attaches to the lower limbs of the human body to augment or assist with the walking ability of the wearer. In order to improve the wearer’s walking ability, the robot senses the wearer’s walking locomotion and classifies it into a gait-phase state, after which it drives the appropriate robot motions for each state using its actuators. This paper presents a method by which the robot senses the wearer’s locomotion along with a novel classification algorithm which classifies the sensed data as a gait-phase state. The robot determines its control mode using this gait-phase information. If erroneous information is delivered, the robot will fail to improve the walking ability or will bring some discomfort to the wearer. Therefore, it is necessary for the algorithm constantly to classify the correct gait-phase information. However, our device for sensing a human’s locomotion has very sensitive characteristics sufficient for it to detect small movements. With only simple logic like a threshold-based classification, it is difficult to deliver the correct information continually. In order to overcome this and provide correct information in a timely manner, a probabilistic gait-phase classification algorithm is proposed. Experimental results demonstrate that the propose