Upper limb motion capturing and classification for unsupervised stroke rehabilitation

In recent years, substantial amount of researches have been carried out on cost-effective remote/unsupervised stroke rehabilitation methods due to the increasing number of post-stroke hospitalisation and the healthcare expenditure associated. This leads to the need of a reliable remote monitoring scheme that can assists medical specialists in monitoring the condition of patients. The information required to provide this remote monitoring system include the general physiological signals and patient's movement during the exercise. This information can be recorded and analysed to assess the patient condition.. Most of the conventional motion-capturing methods are visual based where large and expensive equipments are required and the tracking is generally limited in a certain area by the vision of the optical sensor. The non-visual based methods using inertial sensor suffer from drifting problem even when high-precision accelerometers and gyroscopes are used.