@ARTICLE{ iet:/content/journals/10.1049/htl.2014.0091, author = {Vijayalakshmi Ahanathapillai}, affiliation = { International Digital Laboratory, Institute of Digital Healthcare – WMG, University of Warwick, Coventry, CV4 7AL, UK }, author = {James D. Amor}, affiliation = { Warwick Engineering in Biomedicine, School of Engineering, University of Warwick, Coventry, CV4 7AL, UK }, author = {Zoe Goodwin}, affiliation = { Management Science Department, University of Strathclyde, Glasgow, G1 1XQ, UK }, author = {Christopher J. James}, affiliation = { Warwick Engineering in Biomedicine, School of Engineering, University of Warwick, Coventry, CV4 7AL, UK }, keywords = {wrist wearable unit;USEFIL project;PA parameter;unobtrusive smart environments-for-independent living project;android smart-watch;WWU;independent living project;activity monitoring;assistive technology;assisted living;activity level;}, language = {English}, abstract = {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.}, title = {Preliminary study on activity monitoring using an android smart-watch}, journal = {Healthcare Technology Letters}, issue = {1}, volume = {2}, year = {2015}, month = {February}, pages = {34-39(5)}, publisher ={Institution of Engineering and Technology}, copyright = {This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)}, url = {https://digital-library.theiet.org/;jsessionid=1ca4ms1tbj2a7.x-iet-live-01content/journals/10.1049/htl.2014.0091} }