LAUSR

Social Internet of Things is assumed to provide health services by incorporating social networks and the Internet of Things (IoT). Although, much development in therapy monitoring has been observed recently, few advancements have been achieved in the domain of in-home therapy. Existing industrial and medical solutions require complex and expensive hardware and software that are impractical for home use. Another challenge for in-home therapy is that therapists cannot confirm whether patients are conducting the therapy correctly and for the prescribed number of times. To address these challenges, we propose the multisensor therapy (m-Therapy) framework, in which multiple gesture-tracking sensors and environmental sensors are used to collect therapy and ambient data. The m-Therapy framework compresses the collected data and uploads to a big data server. The framework uses a model of the therapy to guide a patient performing therapy exercises outside medical institutions and even at home. Ambient IoT sensors can help maintain an appropriate ambient environment, which is generally maintained at the medical institutions. We have developed analytics that can provide live or statistical kinematic data, including rotational and angular range of motion of the joints of interest, and ambient environmental data, which can be shared with therapists and caregivers. We present our findings, which shows that the proposed m-Therapy monitoring system can be deployed in real-life scenarios.