Discriminating and Measuring Activities of Daily Living

Newman, Jacob L. ORCID: https://orcid.org/0000-0002-9149-6181, Collins, Jennifer and Phillips, John S. (2025) Discriminating and Measuring Activities of Daily Living. In: 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2025-07-14.

PDF (EMBC_FINAL) - Accepted Version Restricted to Repository staff only until 31 December 2099. Request a copy

Abstract

Activities of Daily Living (ADLs) are used to assess the ability of individuals to look after themselves in the home, and can be used to aid the diagnosis of degenerative brain diseases, such as Alzheimer’s Disease. The current methods for assessing ADLs require the subjective input of a human, and as such may not reflect the objective capabilities of the individual. Therefore, this work explores the automatic and objective quantification of ADLs using data obtained from a wearable medical device which records eye- and head-movements. We focus on detecting four basic activities including eating, brushing teeth, walking and inactivity. Using a subject-independent testing framework, in which the data from each individual being tested does not appear in the training data, our main contribution is to explore a range of contemporary approaches to time series classification and to compare them to our own previous work in this area. Using a MUSE classifier, we obtain a peak mean accuracy of 84.72%. We also present a system for classifying the duration of each activity, which we suggest may be a good parameter for determining how well an activity has been performed. In this task, we achieve perfect discrimination between three durations of 30 s, 60 s and 90 s. Clinical relevance Detailed measurements of activities of daily living could aid the diagnosis, monitoring and care of individuals with degenerative brain conditions.

Item Type:	Conference or Workshop Item (Paper)
Faculty \ School:	Faculty of Science > School of Computing Sciences Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Centres > Population Health (former - to 2025) Faculty of Medicine and Health Sciences > Research Centres > Public Health Faculty of Science > Research Groups > Health Computing
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	28 Apr 2025 15:30
Last Modified:	07 Aug 2026 13:38
URI:	https://ueaeprints.uea.ac.uk/id/eprint/99130
DOI:	10.1109/EMBC58623.2025.11251699

Actions (login required)

View Item