Capturing the movements of a patient performing a rehabilitation exercise currently involves an extensive lab setup. The goal of this study is to investigate whether a 3D camera, such as the Microsoft Kinect (TM), can be used to monitor patients locally. Specifically we are interested in the lower limbs since most 3D camera algorithms focus on the upper body while for rehabilitation, the lower body is crucial. This paper presents two particle-filtering based algorithms for accurate tracking. The first algorithm estimates the configuration of the lower limbs simultaneously while the second one estimates the configuration of one limb at a time. We compare our estimates with a gold standard and find that we are able to recognize most movement characteristics. Furthermore, our approach is better at tracking the height of the foot and yields more stable tracking results than the NITE skeleton tracker. ispartof: pages:1-16 ispartof: 1st International Workshop on Machine Learning for Urban Sensor Data pages:1-16 ispartof: SenseML Workshop at ECML/PKDD location:Nancy date:15 Sep - 19 Sep 2014 status: published
