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Supporting older people, many of whom live with chronic conditions or cognitive and physical impairments, to live independently at home is of increasing importance due to ageing demographics. To aid independent living at home, much effort is being directed at reliably detecting activities from sensor data to monitor people’s quality of life or to enhance self-management of their own health. Current efforts typically leverage smart homes which have large numbers of sensors installed to overcome challenges in the accurate detection of activities. In this work, we report on the results of machine learning models based on data collected with a small number of low-cost, off-the-shelf passive sensors that were retrofitted in real homes, some with more than a single occupant. Models were developed from the sensor data collected to recognize activities of daily living, such as eating and dressing as well as meaningful activities, such as reading a book and socializing. We evaluated five algorithms and found that a Recurrent Neural Network was most accurate in recognizing activities. However, many activities remain difficult to detect, in particular meaningful activities, which are characterized by high levels of individual personalization. Our work contributes to applying smart healthcare technology in real-world home settings.
O'Sullivan, D., Basaru, R., Stumpf, S., & Maiden, N. (2021). Monitoring Quality of Life Indicators at Home from Sparse and Low-Cost Sensor Data. Technological University Dublin. DOI: 10.21427/5350-MA43