Towards a context-aware biofeedback activity recommendation mobile application for healthy lifestyle

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In this paper, we propose a mobile application that provides users with physical activity recommendations on a daily basis. The increasing percentage of physical inactivity among individuals [1] is the main motivation behind this system. Thus, this system represents one possible solution to counter this issue by recommending the minimum threshold of daily physical activity required for individuals to maintain an active lifestyle. The system performs this task by utilizing a biofeedback sensor (accelerometer) that tracks user's movements. Users' calorie consumption is calculated from acceleration data and is then used as a foundation for system recommendations. Also, context data is utilized to provide context-aware recommendations, which will make the suggested activities from our system easier to follow for individuals. The proposed system gives recommendations in the forms of textual, audio and haptic recommendations. A questionnaire has been conducted in order to estimate the importance of the proposed system through estimating participants' daily activity levels. Additionally, it aims to determine context data that can support the proposed system most fully. Results upheld the proposed system with 46% of participants who exercise for less than 15 minutes daily. Our findings also show that weather conditions and work commitments are the factors, which have the strongest negative impact on an individual's physical activity. © 2013 The Authors.




Badawi, H., & El Saddik, A. (2013). Towards a context-aware biofeedback activity recommendation mobile application for healthy lifestyle. In Procedia Computer Science (Vol. 21, pp. 382–389). Elsevier B.V.

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