eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers

Research Article

Stress Detection Using Smart Phone Data

  • @INPROCEEDINGS{10.1007/978-3-319-49655-9_41,
        author={Panagiotis Kostopoulos and Athanasios Kyritsis and Michel Deriaz and Dimitri Konstantas},
        title={Stress Detection Using Smart Phone Data},
        proceedings={eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers},
        keywords={Stress detection Smartphone Sleeping pattern Social interaction Physical activity},
  • Panagiotis Kostopoulos
    Athanasios Kyritsis
    Michel Deriaz
    Dimitri Konstantas
    Year: 2017
    Stress Detection Using Smart Phone Data
    DOI: 10.1007/978-3-319-49655-9_41
Panagiotis Kostopoulos1,*, Athanasios Kyritsis1,*, Michel Deriaz1,*, Dimitri Konstantas1,*
  • 1: University of Geneva
*Contact email:,,,


In today’s society, working environments are becoming more stressful. The problem of occupational stress is generally recognized as one of the major factors leading to a wide spectrum of health problems. However work should, ideally, be a source of health, pride and happiness, in the sense of enhancing motivation and strengthening personal development. In this work, we present StayActive, a system which aims to detect stress and burn-out risks by analyzing the behaviour of the users via their smartphone. The main purpose of StayActive is the use of the mobile sensor technology for detecting stress. Then a mobile service can recommend and present various relaxation activities “just in time” in order to allow users to carry out and solve everyday tasks and problems at work. In particular, we collect data from people’s daily phone usage gathering information about the sleeping pattern, the social interaction and the physical activity of the user. We assign a weight factor to each of these three dimensions of wellbeing according to the user’s personal perception and build a stress detection system. We evaluate our system in a real world environment with young adults and people working in the transportation company of Geneva. This paper highlights the architecture and model of this innovative stress detection system. The main innovation of this work is addressed in the fact that the way the stress level is computed is as less invasive as possible for the users.