Learning of Arm Exercise Behaviors: Assistive Therapy based on Therapist-Patient Observation
Howard, Ayanna M.
Park, Hae Won
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Machine learning techniques have currently been deployed in a number of real-world application areas – from casino surveillance to fingerprint matching. That fact, coupled with advances in computer vision and human-computer interfaces, positions systems that can learn from human observation at the point where they can realistically and reliably be deployed as functional components in autonomous control systems. Healthcare applications though pose a unique challenge in that, although autonomous capability might be available, it might not be desired. And yet, based on recent studies focused on assessment of the changing demographics of the world, there is a need for technology that can deal with the shortcomings envisioned in the workforce. Traditional roles for robotics have focused on repetitive, hazardous or dull tasks. If we take the same stance on healthcare applications, we find that some therapeutic activities fall under this traditional classification due to the long-repetitive nature of the therapist-patient interaction. As such, in this paper, we discuss techniques that can be used to model exercise behavior by observing the patient during therapist-patient interaction. The ultimate goal is to monitor patient performance on repetitive exercises, possibly over the course of multiple days between therapy sessions