Preliminary study on activity monitoring using an android smart-watch

Healthcare Technology Letters

article

Vijayalakshmi Ahanathapillai¹  James D. Amor²  Zoe Goodwin³  Christopher J. James² 

[1] International Digital Laboratory, Institute of Digital Healthcare – WMG, University of Warwick;Warwick Engineering in Biomedicine, School of Engineering, University of Warwick;Management Science Department, University of Strathclyde

关键词: assisted living;    health care;    mobile computing;    patient monitoring;    activity monitoring;    android smart-watch;    independent living project;    USEFIL project;    unobtrusive smart environments-for-independent living project;    assistive technology;    wrist wearable unit;    WWU;    assisted living;    PA parameter;    activity level;   

DOI  :  10.1049/htl.2014.0091

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

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.

【 授权许可】

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