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

机译：使用IoMT和大数据分析技术实现环境辅助生活的智能医疗框架

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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. (C) 2019 Elsevier B.V. All rights reserved.
机译：在普适计算时代，通过IoMT（医疗物联网），可穿戴传感器和电信技术的最新进步，人类生活变得更加智能，以提供智能医疗服务。 IoMT具有革新医疗行业的潜力。 IoMT通过软件和ICT（信息和通信技术）将可穿戴传感器，患者，医疗保健提供者和看护者互连。 AAL（环境辅助生活）使新技术的集成成为我们日常生活的一部分。在本文中，我们为AAL提供了一种新颖的智能医疗保健框架，该工具使用IoMT和智能机器学习算法监控老年人的身体活动，以便更快地进行分析，制定决策并提供更好的治疗建议。数据是从放置在受试者左脚踝，右臂和胸部上的多个可穿戴传感器收集的，并通过IoMT设备传输到集成的云和数据分析层。为了并行处理大量数据，使用了Hadoop MapReduce技术。适用于MapReduce范例的多项式朴素贝叶斯分类器用于识别不同身体部位所经历的运动，与串行处理器相比，通过并行处理可提供更高的可伸缩性和更好的性能。我们提出的框架预测了12项体育活动，总体准确性为97.1％。这可以被认为是识别体育活动以远程监测老年人健康状况的最佳解决方案。 （C）2019 Elsevier B.V.保留所有权利。

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