Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques

Abstract In the era of pervasive computing, human living has become smarter by the latest advancements in IoMT (Internet of Medical Things), wearable sensors and telecommunication technologies in order to deliver smart healthcare services. IoMT has the potential to revolutionize the healthcare industry. IoMT interconnects wearable sensors, patients, healthcare providers and caregivers via software and ICT (Information and Communication Technology). AAL (Ambient Assisted Living) enables integration of new technologies to be part of our daily life activities. In this paper, we have provided a novel smart healthcare framework for AAL to monitor the physical activities of elderly people using IoMT and intelligent machine learning algorithms for faster analysis, decision making and better treatment recommendations. Data is collected from multiple wearable sensors placed on subject’s left ankle, right arm, and chest, is transmitted through IoMT devices to the integrated cloud and data analytics layer. To process huge amounts of data in parallel, Hadoop MapReduce techniques are used. Multinomial Naive Bayes classifier, which fits into the MapReduce paradigm, is utilized to recognize the motion experienced by different body parts and provides higher scalability and better performance with parallel processing when compared to serial processor. Our proposed framework predicts 12 physical activities with an overall accuracy of 97.1%. This can be considered as an optimal solution for recognizing physical activities to remotely monitor health conditions of elderly people.

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