Abstract
In this study, we present a low-cost, flexible and data-driven intelligent system which could monitor and determine the wellness conditions of the elderly living in a nursing home in relation to their daily activities. Changes of daily activities are obtained in real time for reasonable forecasting of wellness indices. These tasks are achieved by a framework integrating spatial and temporal contextual information for determining the wellness of the elderly. The daily activities of the elderly are detected through the location information collected by the Radio Frequency Identification (RFID) technology. A Support Vector Machine (SVM) model is trained using the activity data of 5 different elderly people living in a nursing home, and the results show that it performs well in forecasting wellness indices of the elderly.
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Acknowledgement
The authors would like to express their gratitude to all the subjects that participated in the experiments. This study is supported by Science and Technology Innovation Project of Foshan City, China (Grant No. 2015IT100095) and Science and Technology Planning Project of Guangdong Province, China (Grant No. 2016B010108002).
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Wu, Y., Liu, L., Kang, J., Li, L., Huang, B. (2017). Measuring the Wellness Indices of the Elderly Using RFID Sensors Data in a Smart Nursing Home. In: Song, S., Renz, M., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10612. Springer, Cham. https://doi.org/10.1007/978-3-319-69781-9_7
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DOI: https://doi.org/10.1007/978-3-319-69781-9_7
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