<i>Energy per Operation</i> Optimization for Energy-Harvesting Wearable IoT Devices
Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Mini...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/3/764 |
| _version_ | 1817988931216474112 |
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| author | Jaehyun Park Ganapati Bhat Anish NK Cemil S. Geyik Umit Y. Ogras Hyung Gyu Lee |
| author_facet | Jaehyun Park Ganapati Bhat Anish NK Cemil S. Geyik Umit Y. Ogras Hyung Gyu Lee |
| author_sort | Jaehyun Park |
| collection | DOAJ |
| description | Wearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4× compared to a manual optimization. |
| first_indexed | 2024-04-14T00:39:51Z |
| format | Article |
| id | doaj.art-ad4c9b75761b437e8104b3ae00f86bf8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T00:39:51Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ad4c9b75761b437e8104b3ae00f86bf82022-12-22T02:22:13ZengMDPI AGSensors1424-82202020-01-0120376410.3390/s20030764s20030764<i>Energy per Operation</i> Optimization for Energy-Harvesting Wearable IoT DevicesJaehyun Park0Ganapati Bhat1Anish NK2Cemil S. Geyik3Umit Y. Ogras4Hyung Gyu Lee5School of Electrical Engineering, University of Ulsan, Ulsan 44610, KoreaSchool of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281, USASchool of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281, USATechnology Development, Intel Corporation, Chandler, AZ 85226, USASchool of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281, USASchool of Computer and Communication Engineering, Daegu University, Gyeongsan-si 38453, KoreaWearable internet of things (IoT) devices can enable a variety of biomedical applications, such as gesture recognition, health monitoring, and human activity tracking. Size and weight constraints limit the battery capacity, which leads to frequent charging requirements and user dissatisfaction. Minimizing the energy consumption not only alleviates this problem, but also paves the way for self-powered devices that operate on harvested energy. This paper considers an energy-optimal gesture recognition application that runs on energy-harvesting devices. We first formulate an optimization problem for maximizing the number of recognized gestures when energy budget and accuracy constraints are given. Next, we derive an analytical energy model from the power consumption measurements using a wearable IoT device prototype. Then, we prove that maximizing the number of recognized gestures is equivalent to minimizing the duration of gesture recognition. Finally, we utilize this result to construct an optimization technique that maximizes the number of gestures recognized under the energy budget constraints while satisfying the recognition accuracy requirements. Our extensive evaluations demonstrate that the proposed analytical model is valid for wearable IoT applications, and the optimization approach increases the number of recognized gestures by up to 2.4× compared to a manual optimization.https://www.mdpi.com/1424-8220/20/3/764wearable devicesgesture recognitionenergy modelenergy harvestingenergy optimization |
| spellingShingle | Jaehyun Park Ganapati Bhat Anish NK Cemil S. Geyik Umit Y. Ogras Hyung Gyu Lee <i>Energy per Operation</i> Optimization for Energy-Harvesting Wearable IoT Devices Sensors wearable devices gesture recognition energy model energy harvesting energy optimization |
| title | <i>Energy per Operation</i> Optimization for Energy-Harvesting Wearable IoT Devices |
| title_full | <i>Energy per Operation</i> Optimization for Energy-Harvesting Wearable IoT Devices |
| title_fullStr | <i>Energy per Operation</i> Optimization for Energy-Harvesting Wearable IoT Devices |
| title_full_unstemmed | <i>Energy per Operation</i> Optimization for Energy-Harvesting Wearable IoT Devices |
| title_short | <i>Energy per Operation</i> Optimization for Energy-Harvesting Wearable IoT Devices |
| title_sort | i energy per operation i optimization for energy harvesting wearable iot devices |
| topic | wearable devices gesture recognition energy model energy harvesting energy optimization |
| url | https://www.mdpi.com/1424-8220/20/3/764 |
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