A Sensor Platform for Non-invasive Remote Monitoring of Older Adults in Real Time

Bennasar, M. and McCormick, C. and Price, B. and Gooch, D. and Stuart, A. and Mehta, V. and Clare, L. and Bennaceur, A. and Cohen, J. and Bandara, A. and Levine, M. and Nuseibeh, B. (2019) A Sensor Platform for Non-invasive Remote Monitoring of Older Adults in Real Time. In: Innovation in Medicine and Healthcare Systems, and Multimedia - Proceedings of KES-InMed 2019 and KES-IIMSS 2019 Conferences. Smart Innovation, Systems and Technologies . Springer Singapore, Singapore, pp. 125-135. ISBN 9789811385650

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Abstract

The population of older adults is increasing across the globe; this growth is predicted to continue into the future. Most older adults prefer to live in their own home, but many live alone without immediate support. Living longer is often coupled with health and social problems and difficulty managing daily activities. Therefore, some level of care is required, but this is costly. Technological solutions may help to mitigate these problems by recognising subtle changes early and intervening before problems become unmanageable. Understanding a person’s usual behaviour when carrying out Activities of Daily Living (ADL) makes it possible to detect and respond to anomalies. However, current commercial and research monitoring systems do not offer an analysis of ADL and are unable to detect subtle changes. To address this gap, we propose the STRETCH (Socio-Technical Resilience for Enhancing Targeted Community Healthcare) sensor platform that is comprised of non-invasive sensors and machine learning techniques to recognise changes and allow early interventions. The paper discusses design principles, modalities, system architecture, and sensor network architecture.

Item Type: Contribution in Book/Report/Proceedings
Subjects:
Departments: Faculty of Science and Technology > Psychology
ID Code: 135372
Deposited By: ep_importer_pure
Deposited On: 08 Aug 2019 10:30
Refereed?: Yes
Published?: Published
Last Modified: 25 Feb 2020 05:31
URI: https://eprints.lancs.ac.uk/id/eprint/135372

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