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  • 1. Li, W.
    et al.
    Zhu, Chunsheng
    Yang, Laurence
    Shu, L.
    Ngai, Edith
    Ma, Y.
    Subtasks Scheduling for Distributed Robots in Cloud Manufacturings2015In: IEEE Systems Journal, ISSN 1932-8184, E-ISSN 1937-9234Article in journal (Refereed)
  • 2. Li, Wenxiang
    et al.
    Zhu, Chunsheng
    Yang, Laurence T.
    Shu, Lei
    Ngai, Edith C.-H.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Ma, Yajie
    Subtask scheduling for distributed robots in cloud manufacturing2017In: IEEE Systems Journal, ISSN 1932-8184, E-ISSN 1937-9234, Vol. 11, no 2, p. 941-950Article in journal (Refereed)
  • 3.
    McNamara, Liam
    et al.
    Swedish Inst Comp Sci, SE-16451 Stockholm, Sweden.
    Ngai, Edith
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    SADHealth: A personal mobile sensing system for seasonal health monitoring2018In: IEEE Systems Journal, ISSN 1932-8184, E-ISSN 1937-9234, Vol. 12, no 1, p. 30-40Article in journal (Refereed)
    Abstract [en]

    People's health, mood, and activities are closely related to their environment and the seasons. Countries at extreme latitudes (e.g., Sweden, U.K., and Norway) experience huge variations in their light levels, impacting the population's mental state, well-being and energy levels. Advanced sensing technologies on smartphones enable nonintrusive and longitudinal monitoring of user states. The collected data make it possible for healthcare professionals and individuals to diagnose and rectify problems caused by seasonality. In this paper, we present a personal mobile sensing system that exploits technologies on smartphones to efficiently and accurately detect the light exposure, mood, and activity levels of individuals. We conducted a 2-year experiment with many users to test the functionality and performance of our system. The results show that we can obtain accurate light exposure estimation by opportunistically measuring light data on smartphones, tracking both personal light exposure and the general seasonal trends. An optional questionnaire also provides insight into the correlation between a user's mood and energy level. Our system is able to inform users how little light they are experiencing in the winter time. It can also correlate light exposure data with reduced mood and energy, and provide quantitative measurements for lifestyle changes.

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