Classification of Human Movements with Motion Capture Data in a Motor Rehabilitation Context

Abstract

Augmented and virtual reality can be used in motor or neuromotor rehabilitation clinics to make patients become more motivated and engaged with the treatment. The interaction with the applications stimulates the patient to exercise the impaired limb while enjoying the experience. This work takes the real-time tracking data generated from optical and wearable motion capture devices and uses it to feed machine learning algorithms. The data processing makes the movements with different durations consistent and enables the convergence of the models. Also, the data format is independent of the camera position and user. One of the experiments presented recognizes eight movements being executed in the system.