Stroke patients walk pattern prediction and tracking control method for a novel intelligent lower limb rehabilitation walker

Abstract

Aim to accelerate the process of lower limb rehabilitation for post-stroke people, various intelligent lower limb rehabilitation walkers (ILLRW) with body kinetic analysis and feedback control function have been developed to reduce the nursing workload and enhance training performance. In this work, a novel ILLRW is proposed, equipped with a walking remote system via control lever and driver units to assist user's walk correspondingly. However, the walking ability varied significantly from stroke persons, and a system that can interfere with the driving system to adjust the speed according to the pace is critical. Thus, we also developed a walk pattern prediction system (WPPS) to feedback and further optimize the control system. The WPPS includes a laser range finder (LRF) array and plantar pressure sensors at the bottom, wearable pressure sensors at the user's waist, and an Inertial Measurement Unit (IMU) at the user's body joints. Altogether, the data get further analyzed and a walk pattern model generated while walking, and it is expected that the movement of the walker can relatively match the user's pace. Experimental result shows that our WPPS can track stroke patient's movement pattern effectively.