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An ultra-low-power human body motion sensor using static electric field sensing

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Shwetak N. PatelAuthors Info & Claims

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Pages 99 - 102

https://doi.org/10.1145/2370216.2370233

Published: 05 September 2012 Publication History

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Abstract

Wearable sensor systems have been used in the ubiquitous computing community and elsewhere for applications such as activity and gesture recognition, health and wellness monitoring, and elder care. Although the power consumption of accelerometers has already been highly optimized, this work introduces a novel sensing approach which lowers the power requirement for motion sensing by orders of magnitude. We present an ultra-low-power method for passively sensing body motion using static electric fields by measuring the voltage at any single location on the body. We present the feasibility of using this sensing approach to infer the amount and type of body motion anywhere on the body and demonstrate an ultra-low-power motion detector used to wake up more power-hungry sensors. The sensing hardware consumes only 3.3 μW, and wake-up detection is done using an additional 3.3 μW (6.6 μW total).

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Cited By

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Bian SStrahinja RSchilk PMarc-André CCortesi SDheman KReinschmidt EMagno MKostakos VKay JHoang T(2024)Earable and Wrist-worn Setup for Accurate Step Counting Utilizing Body-Area Electrostatic SensingCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678468(904-910)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678468
Bian SLiu MZhou BLukowicz PMagno M(2024)Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer InteractionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435558:1(1-49)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643555
Cui MXie BWang QXiong JGanesan DShi W(2024)EVLeSen: In-Vehicle Sensing with EV-Leaked SignalProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649389(679-693)Online publication date: 29-May-2024
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An ultra-low-power human body motion sensor using static electric field sensing
1. Information systems
  1. Information systems applications

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Published In

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

September 2012

1268 pages

ISBN:9781450312240

DOI:10.1145/2370216

General Chair:
Anind K. Dey
Carnegie Mellon University
,
Program Chairs:
Hao-Hua Chu
National Taiwan University, Taiwan
,
Gillian Hayes
University of California, Irvine

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2012

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Ubicomp '12

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Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing

September 5 - 8, 2012

Pennsylvania, Pittsburgh

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UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

View all

Bian SStrahinja RSchilk PMarc-André CCortesi SDheman KReinschmidt EMagno MKostakos VKay JHoang T(2024)Earable and Wrist-worn Setup for Accurate Step Counting Utilizing Body-Area Electrostatic SensingCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678468(904-910)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678468
Bian SLiu MZhou BLukowicz PMagno M(2024)Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer InteractionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435558:1(1-49)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643555
Cui MXie BWang QXiong JGanesan DShi W(2024)EVLeSen: In-Vehicle Sensing with EV-Leaked SignalProceedings of the 30th Annual International Conference on Mobile Computing and Networking10.1145/3636534.3649389(679-693)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3636534.3649389
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Fan YPietroni NFerguson S(2023)A Neural Network-based Low-cost Soft Sensor for Touch Recognition and Deformation CaptureProceedings of the 2023 ACM Designing Interactive Systems Conference10.1145/3563657.3595963(889-903)Online publication date: 10-Jul-2023
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Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey

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