From fall detection to stress pattern using smart devices

Diplôme Thèse From fall detection to stress pattern using smart devices eng 2017-10-19 author Kostopoulos Panagiotis 820103 director Konstantas Dimitri 23437 director Deriaz Michel 504335 10.13097/archive-ouverte/unige:99263 urn:nbn:ch:unige-992634 GSEM 47 In this thesis we present two e-health applications. We begin with a practical real time fall detection system running on a smartwatch called F2D. In F2D data from the accelerometer is collected, passing through an adaptive threshold-based algorithm which detects patterns corresponding to a fall. A decision module takes into account the residual movement of the user, matching a detected fall pattern to an actual fall. To the best of our knowledge, this is the first fall detection system which works on a smartwatch, being less stigmatizing for the end user. The next implementation of our expertise and second main element of this thesis is the detection of stress patterns by analyzing smartphone data. We present a novel stress detection system which aims to detect stress and burn-out risks by analyzing the behaviors of the users via their smartphone. Fall detection Stress detection E-health Dewey 025.06/650 Dewey 025.063 13 Centre universitaire d'informatique 0D3 118 Information Science Institute S6 false 16cedea2-168e-49c9-aabf-2cd1485e4afc Thesis Restricted P.Kostopoulos-PhD.pdf 2733026 application/pdf c6fa51163e5618cb8dea7952daa4a434c84c1d383fc8af8f7e94f35f59cb3c6e restrictedAccess Centres et instituts / Centre universitaire d'informatique Faculté d'économie et de management / Information Science Institute

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Kostopoulos, Panagiotis