Tracking Context-Aware Well-Being through Intelligent Environments
Abstract The growth of personal sensors and the ability to sensorize attributes connected with the physical beings and environments are increasing. Initiatives such as Internet of Things (IoT)) aim to connect devices and people through communication channels in order to automate and fuel interaction. Targeted approaches can be found on the Smart Cities projects which use the IoT to gather data from people and attributes related to city management. Though good for management of new cities, well-being should as well be of principal importance. It regards users higher than infrastructure and managerial data. Taking lessons from ergonomic studies, health studies and user habits it is possible to track and monitor user daily living. Moreover, the link between user living conditions and sparse events such as illness, indispositions can be tracked to well-being data through autonomous services. Such application is detailed in the approach categorized by this article and the research presented
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Charles, K. E. (2003). Fanger ’ s Thermal Comfort and Draught Models Fanger ’ s Thermal Comfort and Draught Models IRC Research Report RR-162. October, 29. doi:IRC Research Report RR-162
Choi, J., Ahmed, B., & Gutierrez-Osuna, R. (2010). Ambulatory Stress Monitoring with Minimally-Invasive Wearable Sensors. Comput. Sci. and Eng., Texas A&M. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.188.4605&rep=rep1&type=pdf
Fanger, P. O. (1970). Thermal comfort: Analysis and applications in environmental engineering. Danish Technical Press. Retrieved from http://books.google.pt/books?id=S0FSAAAAMAAJ
Höppe, P. (1999). The physiological equivalent temperature - a universal index for the biometeorological assessment of the thermal environment. International Journal of Biometeorology, 43(2), 71–75. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/10552310 http://dx.doi.org/10.1007/s004840050118
Kr?o, S., Fernandes, J., & Sanchez, L. (2013). SmartSantander–a smart city experimental platform. Electrotechnical Review, 3–6.
Longo, M., Roscia, M., & Lazaroiu, G. C. (2014). Innovating Multi-agent Systems Applied to Smart City, 7(20), 4296–4302.
Parsons, K. (2010). Human thermal environments: the effects of hot, moderate, and cold environments on human health, comfort and performance. Taylor &
Francis. Retrieved from http://books.google.pt/books?id=4oxA6W_Os50C
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Silva, F., Analide, C., Rosa, L., Felgueiras, G., & Pimenta, C. (2013). Social Networks Gamification for Sustainability Recommendation Systems. In Distributed Computing and Artificial Intelligence (pp. 307–315). Springer.
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Solanas, A., Patsakis, C., Conti, M., Vlachos, I., Ramos, V., Falcone, F., … Martinez-Balleste, A. (2014). Smart health: A context-aware health paradigm within smart cities. IEEE Communications Magazine, 52(8), 74–81. doi:10.1109/MCOM.2014.6871673
Steele, R., & Clarke, A. (2013). The Internet of Things and Next-generation Public Health Information Systems. Communications and Network, 05(03), 4–9. doi:10.4236/cn.2013.53B1002 http://dx.doi.org/10.4236/cn.2013.53B1002
Tauseef, M. (2012). Human Emotion Recognition Using Smart Sensors. Massey University.
Silva, F., & Analide, C. (2015). Tracking Context-Aware Well-Being through Intelligent Environments. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 4(2), 61–72. https://doi.org/10.14201/ADCAIJ2015426172
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