Open AccessPreliminary study on activity monitoring using an android smart‐watch
Author(s) -
Ahanathapillai Vijayalakshmi,
Amor James D.,
Goodwin Zoe,
James Christopher J.
Publication year - 2015
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2014.0091
Subject(s) - computer science , wearable computer , activity recognition , activities of daily living , android (operating system) , independent living , life expectancy , smartwatch , wearable technology , population , assisted living , android application , real time computing , embedded system , artificial intelligence , medicine , gerontology , operating system , environmental health , psychiatry
The global trend for increasing life expectancy is resulting in aging populations in a number of countries. This brings to bear a pressure to provide effective care for the older population with increasing constraints on available resources. Providing care for and maintaining the independence of an older person in their own home is one way that this problem can be addressed. The EU Funded Unobtrusive Smart Environments for Independent Living (USEFIL) project is an assistive technology tool being developed to enhance independent living. As part of USEFIL, a wrist wearable unit (WWU) is being developed to monitor the physical activity (PA) of the user and integrate with the USEFIL system. The WWU is a novel application of an existing technology to the assisted living problem domain. It combines existing technologies and new algorithms to extract PA parameters for activity monitoring. The parameters that are extracted include: activity level, step count and worn state. The WWU, the algorithms that have been developed and a preliminary validation are presented. The results show that activity level can be successfully extracted, that worn state can be correctly identified and that step counts in walking data can be estimated within 3% error, using the controlled dataset.