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Jacob Rodrigues, M., Postolache, O. & Cercas, F. (2020). Physiological and behavior monitoring systems for smart healthcare environments: a review. Sensors. 20 (8)
M. C. Rodrigues et al., "Physiological and behavior monitoring systems for smart healthcare environments: a review", in Sensors, vol. 20, no. 8, 2020
@article{rodrigues2020_1714952114913, author = "Jacob Rodrigues, M. and Postolache, O. and Cercas, F.", title = "Physiological and behavior monitoring systems for smart healthcare environments: a review", journal = "Sensors", year = "2020", volume = "20", number = "8", doi = "10.3390/s20082186", url = "https://www.mdpi.com/1424-8220/20/8/2186" }
TY - JOUR TI - Physiological and behavior monitoring systems for smart healthcare environments: a review T2 - Sensors VL - 20 IS - 8 AU - Jacob Rodrigues, M. AU - Postolache, O. AU - Cercas, F. PY - 2020 SN - 1424-8220 DO - 10.3390/s20082186 UR - https://www.mdpi.com/1424-8220/20/8/2186 AB - Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed ER -