Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

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

Timon, Claire M, Hussey, Pamela orcid logoORCID: 0000-0001-5558-1269, Lee, Hyowon orcid logoORCID: 0000-0003-4395-7702, Murphy, Catriona orcid logoORCID: 0000-0002-3262-1130, Harsh, Vardan Rai 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

Abstract
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 involves visualising a set of automatically detected activities of daily living (ADLs) for each participant. ADL detection allows the incorporation of additional participants whose ADLs are detected without system re-training. Methods: Following a 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 ∼20 IoT sensors in their homes. During the ARC trial, participants 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 used on the combination of data from sensors and participant feedback to improve the automatic ADL detection. Results: Association rule mining was used to detect a range of ADLs for each participant independently of others and 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 their ADLs.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Activities of daily living; internet of things sensors; association rule mining; data visualisation
Subjects:UNSPECIFIED
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 Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:Sage Publications
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 under grant number SFI/12/RC/2289_P2, co-funded by the European Regional Development Fund.
ID Code:28918
Deposited On:17 Aug 2023 13:42 by Vidatum Academic . Last Modified 17 Aug 2023 13:42
Documents

Full text available as:

[thumbnail of 2023timon-et-al-2023-automatically-detecting-activities-of-daily-living-from-in-home-sensors-as-indicators-of-routine.pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Creative Commons: Attribution 4.0
2MB
Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record