Towards Low-cost Robust and Stable Hand Tracking for Exercise Monitoring

Applications for home-based care are rapidly increasing in importance due to spiraling health care and elderly care costs. An important aspect of home-based care is exercises for rehabilitation and improving general health. However, without caregivers supervising these exercises it is difficult to monitor them, i.e., to determine whether the exercises have been performed correctly and for the prescribed duration. In this paper we present the first steps toward a computer-based tool for monitoring hand exercises. Hand exercises are important for various diseases such as Parkinson disease. While many algorithms exist for gesture recognition, most of them do require special set-ups and are difficult to use for very inexperienced users in home-based environments. In this paper we present a robust hand region segmentation method which represents the first step toward a hand-tracking algorithm. Our solution requires no calibration and is easily set-up. We evaluate its robustness with regard to complex backgrounds, changes in illuminations, and different hand colours. Our results indicate that the robust hand region segmentation provides a solid foundation for monitoring hand exercises.