Recognition of upper limb movements for remote health monitoring

In this paper we present two methodologies based on a systematic exploration to recognize three fundamental movements of the human forearm (extension, flexion and rotation) performed during an archetypal activity of daily-living (ADL) - ‘making-a-cup-of-tea’ by four healthy subjects and stroke survivors. The recognition methodologies have been further implemented in hardware (ASIC/FPGA) which can be embedded on a resource constrained WSN node for real-time detection of arm movements. We propose that these techniques could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies such as stroke or cerebral palsy by tracking the number of times a patient performs specific arm movements (e.g. prescribed exercises) with the paretic arm throughout the da