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Estimation of kinetic energy harvesting potential for self-powered wearable devices with 67,000 participants from the UK Biobank

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posted on 08.06.2021, 09:30 by Christopher Beach, Alex Casson
Energy harvesting from human motion can reduce reliance on battery recharging in wearable devices and lead to improved adherence. However, to date, studies estimating energy harvesting potential have largely focused on small scale, healthy, population groups in laboratory settings rather than free-living environments with population level participant numbers. Here, we present the largest scale investigation into energy harvesting potential by utilising the activity data collected in the UK Biobank from over 67,000 participants. This paper presents detailed stratification into how the day of the week and participant age affect harvesting potential, as well as how the presence of conditions (such as diabetes, which we investigate here), may affect the expected energy harvester output. We process accelerometery data using a kinetic energy harvester model to investigate power output at a high temporal resolution. Our results identify key differences between the times of day when the power is available and an inverse relationship between power output and participant age. We also identify that the presence of diabetes substantially reduces energy harvesting output, by over 21%. The results presented highlight a key challenge in wearable energy harvesting: that wearable devices aim to monitor health and wellness, and energy harvesting aims to make devices more energy autonomous, but the presence of medical conditions may lead to substantially lower energy harvesting potential. The findings indicate how it is challenging to meet the required power budget to monitor diseases when energy autonomy is a goal.

Funding

DTP 2016-2017 The University of Manchester

Engineering and Physical Sciences Research Council

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The Wearable Clinic: Connecting Health, Self and Care

Engineering and Physical Sciences Research Council

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History

Email Address of Submitting Author

christopher.beach@manchester.ac.uk

ORCID of Submitting Author

0000-0003-4964-3173

Submitting Author's Institution

The University of Manchester

Submitting Author's Country

United Kingdom