STINT: Selective transmission for low-energy physiological monitoring
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Tao-Yi | - |
dc.contributor.author | Vo, Khyong | - |
dc.contributor.author | Baek, Wongi | - |
dc.contributor.author | Khine, Michelle | - |
dc.contributor.author | Dutt, Nikil | - |
dc.date.accessioned | 2023-03-08T14:49:30Z | - |
dc.date.available | 2023-03-08T14:49:30Z | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/63566 | - |
dc.description.abstract | Noninvasive, and continuous physiological sensing enabled by novel wearable sensors is generating unprecedented diagnostic insights in many medical practices. However, the limited battery capacity of these wearable sensors poses a critical challenge in extending device lifetime in order to prevent omission of informative events. In this work, we exploit the inherent sparsity of physiological signals to intelligently enable selective transmission of these signals and thereby improve the energy efficiency of wearable sensors. We propose STINT, a selective transmission framework that generates a sparse representation of the raw signal based on domain-specific knowledge, and which can be integrated into a wide range of resource-constrained embedded sensing IoT platforms. STINT employs a neural network (NN) for selective transmission: The NN identifies, and transmits only the informative parts of the raw signal, thereby achieving low power operation. We validate STINT and establish its efficacy in the domain of IoT for energy-efficient physiological monitoring, by testing our framework on EcoBP-a novel miniaturized, and wireless continuous blood pressure sensor. Early experimental results on the EcoBP device demonstrate that the STINT-enabled EcoBP sensor outperforms the native platform by 14% of sensor energy consumption, with room for additional energy savings via complementary bluetooth and wireless optimizations. © 2020 Owner/Author. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | STINT: Selective transmission for low-energy physiological monitoring | - |
dc.type | Article | - |
dc.identifier.doi | 10.1145/3370748.3406563 | - |
dc.identifier.bibliographicCitation | ACM International Conference Proceeding Series | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85098290812 | - |
dc.citation.title | ACM International Conference Proceeding Series | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | Bluetooth | - |
dc.subject.keywordAuthor | computation-communication trade-off | - |
dc.subject.keywordAuthor | continuous monitoring | - |
dc.subject.keywordAuthor | edge deep learning | - |
dc.subject.keywordAuthor | IoT healthcare | - |
dc.subject.keywordPlus | Blood pressure | - |
dc.subject.keywordPlus | Diagnosis | - |
dc.subject.keywordPlus | Energy efficiency | - |
dc.subject.keywordPlus | Energy utilization | - |
dc.subject.keywordPlus | Internet of things | - |
dc.subject.keywordPlus | Low power electronics | - |
dc.subject.keywordPlus | Patient monitoring | - |
dc.subject.keywordPlus | Critical challenges | - |
dc.subject.keywordPlus | Domain-specific knowledge | - |
dc.subject.keywordPlus | Neural network (nn) | - |
dc.subject.keywordPlus | Physiological monitoring | - |
dc.subject.keywordPlus | Physiological sensing | - |
dc.subject.keywordPlus | Physiological signals | - |
dc.subject.keywordPlus | Selective transmissions | - |
dc.subject.keywordPlus | Sparse representation | - |
dc.subject.keywordPlus | Wearable sensors | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.