Human Motion Intention Detection for an Active Rail-Driven Rehabilitation System

In this study, we propose a human motion intention detection algorithm for an automated raildriven gait rehabilitation system. The automated rehabilitation system, under development by the authors, provides gait rehabilitation that supports a subject's body weight with an active driving rail and an attachment to the ceiling. To provide proper body weight support and to monitor information according to the gait rehabilitation exercise, detecting the subject's motional intent would be useful. The proposed detection algorithm uses a sensor unit, which includes a three-axis accelerometer, a gyroscope, and a magnetometer. The algorithm begins by estimating the sensor's orientation with the sensor signals; the orientation is then transformed to a global frame from a sensor frame. The global frame acceleration signal is subtracted from 1 g to calculate motional acceleration. A specific cyclic pattern of global vertical acceleration is derived. The cyclic pattern is tracked with a state machine to detect standing up, sitting down, and walking. Standing up and sitting down motions are identified with downward and upward vertical velocity relations. The motions of walking, walking down stairs, and walking up stairs are discriminated with horizontal over vertical velocity ratios. The proposed algorithm was implemented on a PC and its functional feasibility was tested successfully on three healthy subjects.