Abstract
Wearable devices promise to reduce strain on the healthcare system and to improve quality of life for users. However, adoption in healthcare settings is limited due in part to the need for constant battery maintenance; which leads to reduced adherence, more complex operation and missing sections of data. Energy harvesting can reduce the reliance on batteries, but the harvesting potential varies substantially depending on where the harvester is placed. Few previous studies investigating placement have considered the foot as a harvesting site, despite the significant interest in smart-shoes and the intrinsic social discreteness of wearable devices at the foot. We investigate the amount of power that can be harvested from four sites on the human body (wrist, hip, ankle and foot), with 12 participants walking on a treadmill. We analyse the differences in the frequency spectrum at each of these sites and perform a sweep of inertial energy harvester parameters to identify the optimal parameters for each site on the body. By considering both performing the harvesting at the foot, and the frequency distribution of the input spectrum present for the first time, we identify that harvesting at the foot provides multiple benefits: more power is available in total; greater physical size is available (compared to the wrist); lower Q harvesters can provide better broadband response; and the foot is the least sensitive location for changes in frequency of walking rate. For harvesters sized at 100 mm, we find that there is 4.2, 6.4 and 25.7 times more power at the hip, ankle and foot respectively compared to the wrist. Foot based sensors thus provide a promising approach towards future fully battery-free wearable devices, motivating future work to investigate the sensing modalities that are feasible at the foot.
Original language | English |
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Article number | 9258908 |
Pages (from-to) | 208136-208148 |
Number of pages | 13 |
Journal | IEEE Access |
Volume | 8 |
DOIs | |
Publication status | Published - 16 Nov 2020 |
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Accelerometer Data for Energy Harvesting During Walking Estimation
Beach, C. (Contributor) & Casson, A. (Contributor), Mendeley Data, 16 Nov 2020
DOI: 10.17632/5rrxw7y5bj.1, https://data.mendeley.com/datasets/5rrxw7y5bj
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