Understanding the main trends in human behavior is fundamental to developing effective adaptive treatments. Inspired by this insight, this paper presents a mathematical quantification of the change of human behavior following external stimuli. In particular, statistical methods are applied to real physical activity data collected intensively using mobile wearable technologies. We explain the setup of the study conducted with multiple participants. Then, a preprocessing of the collected measurements, required to overcome the hurdles associated with behavioral data, is briefly discussed. Furthermore, we identify a dynamical affine model that approximates humans’ sedentary behavior. The affine model is simple yet insightful. We show results of fitting time-invariant as well as switched models along with a quantification of the prediction errors. Moreover, the effect of various types of treatments on the sedentary behavior of several subjects is investigated. As expected, the results show that people react differently to external stimuli. However, common tendencies are clearly observed. Our findings emphasize the necessity of the application of personalized adaptive intervention. Future research directions are discussed accordingly.

Original languageEnglish (US)
Title of host publication2016 IEEE Conference on Control Applications, CCA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781509007554
StatePublished - Oct 10 2016
Event2016 IEEE Conference on Control Applications, CCA 2016 - Buenos Aires, Argentina
Duration: Sep 19 2016Sep 22 2016

Publication series

Name2016 IEEE Conference on Control Applications, CCA 2016


Conference2016 IEEE Conference on Control Applications, CCA 2016
CityBuenos Aires

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Modeling and Simulation


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