Development of a Wheelchair-Based Upper-Limb Assistance Device Controlled by a Predictive Motion Recognition System

Abstract

Many people lose the ability of upper limb due to accident or disease, which greatly influences their activities of daily living (ADL). In order to improve their quality of life (QoL), we developed a wheelchair-based upper-limb exoskeleton controlled by Kinect-based motion recognition system with predictive algorithm based on Kalman filter to assist upper-limb function in ADL. The Kinect was employed to design a human motion recognition system as the control input of the device. According to the angles data of user’s upper limb detected by Kinect, we established a 3-dimension dynamic model to calculate the necessary torques of each joint, in which we considered about inertia of human. We were able to control the assistance device by a real-time track. In addition, the Kalman filter was considered to overcome the time delay issue in this study. By using the established mathematical model in the prediction model and the updated model, we can predictively supply the assistance for upper-limb based on Kinect sensor.