Pattern analysis in daily physical activity data for personal health management

Jung Hsien Chiang, Pei Ching Yang, Hsuan Tu

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


Purpose: sedentary lifestyles have resulted in an increasing number of people who are at increased risk of various conditions and diseases, including overweight, obesity, and metabolic syndromes. Our objective was to systematically record the daily life journal on a platform to increase the self-awareness and improve the sedentary lifestyle and to assist clinicians in understanding and facilitating patients' daily physical activity. Method: we developed a portable activity pattern recognition system designed to automatically recognize the daily activity habits of users, and provide visualized life logs on the wellness self-management platform for patients and clinicians. Based on the participants' and the clinician's comments, appropriate modifications were made. Results: persuading people to improve their activities during non-working hours can enhance the general physical activity. Since users' smartphones automatically monitor their energy expenditure, healthcare professionals can use these data to assist their patients in addressing health problems stemming from the obesity or metabolic syndromes, thus empowering users to avert or delay the progression of diabetes, cardiovascular disease and other complications. Discussion and conclusions: the clinical pilot study showed the feasibility of applying this persuasive technology to improve the physical activity of overweight people. The limitation of the study is the need for Wi-Fi and 3G environments and a smartphone.

Original languageEnglish
Pages (from-to)13-25
Number of pages13
JournalPervasive and Mobile Computing
Publication statusPublished - 2014 Aug

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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