Human Joint Axis Estimation Algorithm Using a Single Sensor to Recognize the Intention of the Wearer of a Wearable Robot

Abstract

Wearable robots have been developed to aid or substitute human gait locomotion. To assist gait locomotion based on the intention of the wearer, gait pattern analysis is required with a wearable sensor that measures body information, such as the joint angular velocity. However, it is difficult to measure the joint angular velocity precisely because of the attachment position of a sensor that has a curvature and an anatomical human joint axis that is invisible. Therefore, a sensor calibration algorithm that aligns the sensor axis with an anatomical human joint axis is required to provide appropriate assistance to the wearer. Hence, in this study, a new and simple sensor calibration algorithm that uses a single sensor to simplify the robot system and reduce its weight is proposed. The attachment position of the sensor may change because the wearer shakes the body or collides with the ground when walking. Thus, a continuous sensor-compensation algorithm is proposed to correct for the changes in the location of the sensor. The effectiveness of the proposed algorithm is demonstrated through gait locomotion experiments on various paths. In addition to the gait locomotion experiments, the proposed algorithm is applied to a real wearable robot system. An ankle-assist wearable robot experiment reveals that the proposed algorithm can enhance the sensing and recognition performance of the robot system.