Detection and classification of human arm movements for physical rehabilitation

In this paper an arm rehabilitation scenario was set-up to estimate and evaluate the principal activities of this limb by the suggested WBAN-based approach. A couple of wearable sensors mounted on the arm and a fixed node constitute the wireless network. In order to identify and classify the rehabilitation activities an algorithm based on the Received Signal Strength Indicator (RSSI), a parameter already available in the wireless sensor nodes, was applied. As a first attempt, a support vector machine (SVM) learning technique was implemented.