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Keywords: Wearable Health Monitoring, Activity Recognition, Acceleration Sensors, Context Awareness, Decision Trees, Defuzzification, Fuzzification, Human Wearable Health Monitoring Systems, Machine Learning, Ubiquitous Computing, Wireless Sensor Network

Schalk Wilhelm Pienaar1 , Reza Malekian1,2*

1 Department of Electrical, Electronic and Computer Engineering, University of Pretoria, 0002, Pretoria, South Africa
2 Department of Computer Science and Media Technology, Malmö University, 205 06, Malmö, Sweden 2 This email address is being protected from spambots. You need JavaScript enabled to view it.


Abstract

Wearable health monitoring systems, with a context-awareness sensing ability, can greatly improve the way such devices work, both in terms of its accuracy and efficiency. This paper summarises related work previously performed, their advantages and disadvantages, and how they can be incorporated in future work. The related work covers approaches using devices such as smartphones used in isolation, smartphones combined with wristbands and ARM based microcontrollers interfaced with different sensors. Different approaches for optimizing power usage in wireless systems are also investigated. For context awareness, various papers are analyzed to determine existing activity recognition patterns, and approaches to solve common problems experienced in this field. Furthermore, this paper proposes a system that can be considered for future work, combining methods that are found to be meet a certain set of criteria, to develop a device that would be applicable for use by underground mine workers.


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