Monitoring Quality of Life Indicators at Home from Sparse, and Low-Cost Sensor Data

O'Sullivan, D., Basaru, R., Stumpf, S. and Maiden, N. (2021) Monitoring Quality of Life Indicators at Home from Sparse, and Low-Cost Sensor Data. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P. and Riaño, D. (eds.) Artificial Intelligence in Medicine: 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, Virtual Event, June 15–18, 2021, Proceedings. Series: Lecture notes in computer science (12721). Springer: Cham, pp. 157-162. ISBN 9783030772109 (doi: 10.1007/978-3-030-77211-6_17)

Abstract

Supporting older people, many of whom live with chronic conditions, cognitive and physical impairments to live independently at home is of increasing importance due to ageing demographicssss. 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 large numbers of sensors 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 sensor data to recognize activities of daily living, such as eating and dressing as well as meaningful activities, such as reading a book and socializing. We 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.

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Stumpf, Dr Simone
• Authors: O'Sullivan, D., Basaru, R., Stumpf, S., and Maiden, N.
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Springer
• ISBN: 9783030772109
• Published Online: 08 June 2021