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Open AccessArticle

Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone

1
Institute of Information Systems, Innopolis University, Innopolis 420500, Russia
2
Department of Computer Science, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK
3
College of Technological Innovation, Zayed University, Abu Dhabi Campus, Abu Dhabi 144534, UAE
4
University College, Zayed University, Dubai 144534, UAE
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Fahim, M.; Khattak, A.M.; Baker, T.; Chow, F.; Shah, B. Micro-context recognition of sedentary behaviour using smartphone. In Proceedings of the Sixth International Conference on Digital Information and Communication Technology and Its Applications (DICTAP), Konya, Turkey, 21–23 July 2016.
Sensors 2018, 18(3), 874; https://doi.org/10.3390/s18030874
Received: 29 October 2017 / Revised: 8 March 2018 / Accepted: 13 March 2018 / Published: 15 March 2018
(This article belongs to the Special Issue Smart Sensing Technologies for Personalised Coaching)
Sedentary behaviour is increasing due to societal changes and is related to prolonged periods of sitting. There is sufficient evidence proving that sedentary behaviour has a negative impact on people’s health and wellness. This paper presents our research findings on how to mine the temporal contexts of sedentary behaviour by utilizing the on-board sensors of a smartphone. We use the accelerometer sensor of the smartphone to recognize user situations (i.e., still or active). If our model confirms that the user context is still, then there is a high probability of being sedentary. Then, we process the environmental sound to recognize the micro-context, such as working on a computer or watching television during leisure time. Our goal is to reduce sedentary behaviour by suggesting preventive interventions to take short breaks during prolonged sitting to be more active. We achieve this goal by providing the visualization to the user, who wants to monitor his/her sedentary behaviour to reduce unhealthy routines for self-management purposes. The main contribution of this paper is two-fold: (i) an initial implementation of the proposed framework supporting real-time context identification; (ii) testing and evaluation of the framework, which suggest that our application is capable of substantially reducing sedentary behaviour and assisting users to be active. View Full-Text
Keywords: context recognition; self-management; unhealthy sitting habits context recognition; self-management; unhealthy sitting habits
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Fahim, M.; Baker, T.; Khattak, A.M.; Shah, B.; Aleem, S.; Chow, F. Context Mining of Sedentary Behaviour for Promoting Self-Awareness Using a Smartphone. Sensors 2018, 18, 874.

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