6th International Conference on Mobile Computing, Applications and Services

Research Article

User Exercise Pattern Prediction through Mobile Sensing

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  • @INPROCEEDINGS{10.4108/icst.mobicase.2014.257797,
        author={Georgi Kotsev and Le Nguyen and Ming Zeng and Joy Zhang},
        title={User Exercise Pattern Prediction through Mobile Sensing},
        proceedings={6th International Conference on Mobile Computing, Applications and Services},
        publisher={IEEE},
        proceedings_a={MOBICASE},
        year={2014},
        month={11},
        keywords={health user modeling crowd sensing data mining analytics},
        doi={10.4108/icst.mobicase.2014.257797}
    }
    
  • Georgi Kotsev
    Le Nguyen
    Ming Zeng
    Joy Zhang
    Year: 2014
    User Exercise Pattern Prediction through Mobile Sensing
    MOBICASE
    IEEE
    DOI: 10.4108/icst.mobicase.2014.257797
Georgi Kotsev1,*, Le Nguyen1, Ming Zeng1, Joy Zhang1
  • 1: Carnegie Mellon University
*Contact email: georgi.kotsev@sv.cmu.edu

Abstract

Even though the health benefits of regular exercising are well known, an average person has difficulty maintaining physical activity on a regular basis. One of the main reasons for this is lack of motivation. With their increasing ubiquity, wireless devices and smartphones and their sensing capabilities now can be involved in solving this issue. Many mobile applications have been developed with which people are able to keep track of their exercises, become more aware of their physical condition, and be more motivated. The collected data is also a good source for researchers in understanding the exercise patterns and the main factors influencing people to exercise. Understanding those factors will allow better applications to be built, which helps motivate people. In this work, we quantitatively analyze a dataset collected from over 10,000 users. To better understand the user exercise patterns, we identify a set of factors influencing their exercise patterns. Based on these insights, we develop a prediction model to predict users' future exercise activities.