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2018 | OriginalPaper | Buchkapitel

Dynamically Adapting the Environment for Elderly People Through Smartwatch-Based Mood Detection

verfasst von : Antonio Capodieci, Pascal Budner, Joscha Eirich, Peter Gloor, Luca Mainetti

Erschienen in: Collaborative Innovation Networks

Verlag: Springer International Publishing

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Abstract

The ageing population and age-related diseases are some of the most urgent challenges in healthcare. This leads to an increasing demand in innovative solutions to afford a healthy and safe lifestyle to the elderly. Towards this goal, the City4Age project, funded by the Horizon 2020 Programme of the European Commission, focuses on IoT-based personal data capture, supporting smart cities to empower social/health services. This paper describes the combination of the smartwatch-based Happimeter with City4Age data capture technology. Through measuring the mood of the wearer of the smartwatch, a signal is transmitted to the Philips Hue platform, enabling mood-controlled lighting. Philips Hue allows the wireless remote control of energy-efficient LED light bulbs. Thus, measuring the mood through the Happimeter, the living environment for elderly people can be dynamically adapted. We anticipate that by changing colors and brightness of light bulbs using the Philips Hue platform, their quality of life can be improved. A validation test will be done in the context of the City4Age project, involving 31 elderly people living in a Southern Italian city.

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Literatur
Zurück zum Zitat Mainetti, L., Manco, L., Patrono, L., Secco, A., Sergi, I., & Vergallo, R. (2016b). An ambient assisted living system for elderly assistance applications. In 2016 I.E. 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). https://doi.org/10.1109/PIMRC.2016.7794963 Mainetti, L., Manco, L., Patrono, L., Secco, A., Sergi, I., & Vergallo, R. (2016b). An ambient assisted living system for elderly assistance applications. In 2016 I.E. 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). https://​doi.​org/​10.​1109/​PIMRC.​2016.​7794963
Zurück zum Zitat Ricevuti, G., Venturini, L., Copelli, S., Mercalli, F., & Nicolardi, G. (2017). Data driven MCI and frailty prevention: Geriatric modelling in the City4Age project. In 2017 I.E. 3rd International Forum on Research and Technologies for Society and Industry (RTSI) (pp. 1–6). https://doi.org/10.1109/RTSI.2017.8065932 Ricevuti, G., Venturini, L., Copelli, S., Mercalli, F., & Nicolardi, G. (2017). Data driven MCI and frailty prevention: Geriatric modelling in the City4Age project. In 2017 I.E. 3rd International Forum on Research and Technologies for Society and Industry (RTSI) (pp. 1–6). https://​doi.​org/​10.​1109/​RTSI.​2017.​8065932
Metadaten
Titel
Dynamically Adapting the Environment for Elderly People Through Smartwatch-Based Mood Detection
verfasst von
Antonio Capodieci
Pascal Budner
Joscha Eirich
Peter Gloor
Luca Mainetti
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-74295-3_6