Context-Aware Well-Being Assessment in Intelligent Environments

  • Fábio SilvaEmail author
  • Celestino Gonçalves
  • Cesar Analide
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 376)


The implementation of concepts such as smart cities, ambient intelligence and internet of things enables the construction of complex systems that may follow users across environments through many devices. One potential application is the assessment and assurance of well-being of users within different environment with different configurations. This is a complex task that requires the capture of the state and context of both users and environments through sensors dispersed across environments and users. It’s the opportunities created by the emergence of technology that provide enough information to intelligent autonomous systems. Adapting expectations of a well-being assessment system to task and context is possible using the new techniques imported from different fields such as sensor networks, sensor fusion and machine learning. This article encompasses the design and implementation of a platform to evaluate well-being according to each context and translate it to sustainable indicators.


Sensors networks Ambient intelligence Sustainable indicators Well-Being 



This work was developed in the context of the project CAMCoF - Context-aware Multimodal Communication Framework funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through FCT - Fundação para a Ciência e a Tecnologia within project FCOMP-01-0124-FEDER-028980 and PEst-OE/EEI/UI0752/2014. Additionally, it is also supported by a doctoral grant, SFRH/BD/78713/2011, by FCT in the financial program POPH/FSE in Portugal.


  1. 1.
    L. Atzori, A. Iera, G. Morabito, The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefzbMATHGoogle Scholar
  2. 2.
    R. Steele, A. Clarke, The internet of things and next-generation public health information systems. Commun. Netw. 05(03), 4–9 (2013)CrossRefGoogle Scholar
  3. 3.
    A. Solanas, C. Patsakis, M. Conti, I. Vlachos, V. Ramos, F. Falcone, O. Postolache, P. Perez-martinez, R. Pietro, D. Perrea, A. Martinez-Balleste, Smart health: a context-aware health paradigm within smart cities. IEEE Commun. Mag. 52(8), 74–81 (2014)CrossRefGoogle Scholar
  4. 4.
    G. Piro, I. Cianci, L. A. Grieco, G. Boggia, P. Camarda, Information centric services in Smart Cities. J. Syst. Softw. 88, 169–188 (2014)Google Scholar
  5. 5.
    M. Chan, D. Estève, J.-Y. Fourniols, C. Escriba, E. Campo, Smart wearable systems: current status and future challenges. Artif. Intell. Med. 56(3), 137–156 (2012)CrossRefGoogle Scholar
  6. 6.
    R. Rana, B. Kusy, R. Jurdak, J. Wall, W. Hu, Feasibility analysis of using humidex as an indoor thermal comfort predictor. Energy Build. 64, 17–25 (2013)CrossRefGoogle Scholar
  7. 7.
    L. Atallah, B. Lo, G.-Z. Yang, Can pervasive sensing address current challenges in global healthcare? J. Epidemiol. Glob. Health 2(1), 1–13 (2012)CrossRefGoogle Scholar
  8. 8.
    C. Biron, H. Ivers, J.-P. Brun, C.L. Cooper, Risk assessment of occupational stress: extensions of the clarke and Cooper approach. Health. Risk Soc. 8(4), 417–429 (2006)CrossRefGoogle Scholar
  9. 9.
    P.O. Fanger, Thermal Comfort: Analysis and Applications in Environmental Engineering. (Danish Technical Press, Copenhagen 1970)Google Scholar
  10. 10.
    K. Parsons, Human Thermal Environments: The Effects of Hot, Moderate, and Cold Environments on Human Health, Comfort and Performance. (Taylor & Francis, London, 2010)Google Scholar
  11. 11.
    K.C. Parsons, Environmental ergonomics: a review of principles, methods and models. Appl. Ergon. 31(6), 581–594 (2000)CrossRefGoogle Scholar
  12. 12.
    J. Choi, B. Ahmed, R. Gutierrez-Osuna, Ambulatory stress monitoring with minimally-invasive wearable sensors. Comput. Sci. Eng. Texas A&M, (2000)Google Scholar
  13. 13.
    M. Tauseef, Human Emotion Recognition Using Smart Sensors, Massey University, 2012Google Scholar
  14. 14.
    F. Silva, T. Olivares, F. Royo, M.A. Vergara, C. Analide, Experimental Study of the Stress Level at the Workplace Using an Smart Testbed of Wireless Sensor Networks and Ambient Intelligence Techniques, in Natural and Artificial Computation in Engineering and Medical Applications SE - 21, ed. by J. Ferrández Vicente, J. Álvarez Sánchez, F. de la Paz López, F.J. Toledo Moreo, vol. 7931 (Springer, Berlin Heidelberg, 2013), pp. 200–209Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fábio Silva
    • 1
    Email author
  • Celestino Gonçalves
    • 1
  • Cesar Analide
    • 1
  1. 1.Department of InformaticsUniversity of MinhoBragaPortugal

Personalised recommendations