3D Arm Motion Tracking for Home-based Rehabilitation

This paper presents a real-time hybrid solution to articulated 3D arm motion tracking for home-based rehabilitation by combining visual and inertial sensors. The Extended Kalman Filter (EKF) is used to fuse the different data modalities from two sensors and exploit complementary sensor characteristics. Due to the non-linear property of the arm motion tracking, upper limb geometry information and the pin-hole camera model are used to improve the tracking performance. The experimental results show the real-time performance and reasonable accuracy of our system. Recent developments in visual tracking tend to track 3D arm motion using a single camera (Goncalves et al., 1995). Most visual tracking algorithms of arm motion are however computationally expensive and have low tracking accuracy. They normally need manual initialisation, and their motion model or structure model is difficult to generalise. In contrast, inertial tracking has attracted much attention recently in human motion analysis (Bachmann et al, 1999). Most of the work focused on using only accelerometers (or gyros) attached to a human body to detect and analyse the human motion. Zhou and Hu (Zhou and Hu, 2005) used both acceleration and rate of turn to track 3D human arm motion in real time. But inertial tracking suffers from the drift problem. Integrating visual and inertial sensors into a motion tracking system proves to be of particular value to achieve robust and applicable data (Foxlin et al., 2004). But existing work has mainly been done on tracking the pose of a rigid object. Our interest in this paper is to integrate visual and inertial sensors for tracking the 3D motion of articulated objects, e.g., human upper limbs. The purpose is to develop a 3D motion tracking model for home-based rehabilitation projects, which should be cheap, accurate and in real time. Traditionally stroke patients rely on the help of physiotherapists or well-trained carers to diagnose their rehabilitation activities during physiotherapy. We propose to develop an intelligent system to support the stroke patients in doing rehabilitation at home so that the burden on hospitals and