City Research Online

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

O’Sullivan, D., Basaru, R., Stumpf, S. ORCID: 0000-0001-6482-1973 & Maiden, N. ORCID: 0000-0001-6233-8320 (2021). Monitoring Quality of Life Indicators at Home from Sparse, and Low-Cost Sensor Data. Paper presented at the International Conference on Artificial Intelligence in Medicine, 15-18 Jun 2021, Virtual Event. 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 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 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.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: This paper has been published in Lecture Notes in Computer Science, Springer. DOI: 10.1007/978-3-030-77211-6_17 .
Publisher Keywords: Activity Recognition. Sensors, Machine Learning, Independent Living.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RA Public aspects of medicine
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
[img]
Preview
Text - Accepted Version
Download (52kB) | Preview

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login