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Automatically detecting activities of daily living from in-home sensors as indicators of routine behaviour in an older population

Timon, Claire M. orcid logoORCID: 0000-0002-5778-6003, Hussey, Pamela orcid logoORCID: 0000-0003-2840-9361, Murphy, Catriona orcid logoORCID: 0000-0002-3262-1130, Lee, Hyowon orcid logoORCID: 0000-0003-4395-7702, Rai, Harsh Vardan and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2023) Automatically detecting activities of daily living from in-home sensors as indicators of routine behaviour in an older population. Digital Health, 9 . ISSN 2055-2076

Objective: The NEX project has developed an integrated Internet of Things (IoT) system coupled with data analytics to offer unobtrusive health and wellness monitoring supporting older adults living independently at home. Monitoring currently involves visualising a set of automatically detected activities of daily living (ADLs) for each participant. The detection of ADLs is achieved to allow the incorporation of additional participants whose ADLs are detected without re-training the system. Methods: Following an extensive User Needs and Requirements study involving 426 participants, a pilot trial and a friendly trial of the deployment, an Action Research Cycle (ARC) trial was completed. This involved 23 participants over a 10-week period each with c.20 IoT sensors in their homes. During the ARC trial, participants each took part in two data-informed briefings which presented visualisations of their own in-home activities. The briefings also gathered training data on the accuracy of detected activities. Association rule mining was then used on the combination of data from sensors and participant feedback to improve the automatic detection of ADLs. Results: Association rule mining was used to detect a range of ADLs for each participant independently of others and was then used to detect ADLs across participants using a single set of rules for each ADL. This allows additional participants to be added without the necessity of them providing training data. Conclusions: Additional participants can be added to the NEX system without the necessity to re-train the system for automatic detection of the set of their activities of daily living.
Item Type:Article (Published)
Uncontrolled Keywords:Activities of daily living; IoT sensors; association rule mining; data visualisation
Subjects:Computer Science > Artificial intelligence
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
DCU Faculties and Schools > Faculty of Science and Health > School of Nursing, Psychotherapy & Community Health
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Official URL:https://doi.org/10.1177/20552076231184084
Copyright Information:©2023 The Authors.
Funders:Disruptive Technologies Innovation Fund administered by Enterprise Ireland, project grant number DT-2018-0258, Science Foundation Ireland SFI/12/RC/2289_P2, European Regional Development Fund
ID Code:28792
Deposited On:20 Jul 2023 14:11 by Alan Smeaton . Last Modified 20 Jul 2023 14:11

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