research-article

AURES: A Wide-Band Ultrasonic Occupancy Sensing Platform

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Anthony RoweAuthors Info & Claims

BuildSys '16: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments

Pages 157 - 166

https://doi.org/10.1145/2993422.2993580

Published: 16 November 2016 Publication History

Abstract

In this paper, we present a platform designed for low-power real-time sensing of the number of occupants in indoor spaces. The system transmits a wide-band ultrasonic signal into a room and then processes the superposition of the reflections recorded by a microphone. The system has two modes of operation, one for presence detection and one for estimating the number of occupants in a region. The presence detection uses the difference between multiple transmissions in succession with a set of general classifiers that make a binary decision about if the room contains occupants. We then use a semi-supervised learning approach based on Weighted Principal Component Analysis (WPCA) that requires minimal training data to estimate the number of occupants. We also present the design of an energy harvesting embedded platform and demonstrate that our algorithm can continuously execute using energy harvested from indoor solar panels. The platform has a dual Bluetooth Low-Energy and 802.15.4 interface to communicate with a gateway or nearby mobile phone that runs an interface that aids in collecting training data. We evaluate our algorithm on a wide-variety of indoor spaces as well as benchmark the hardware in terms of sampling rate given an energy budget. On more than three weeks of data, we see that we can detect motions with an average of 85% recall rate and perform occupancy counting with an average error of 10% in terms of maximum occupancy.

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Cited By

Chaudhari PXiao YCheng MLi T(2024)Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT SensorsSensors10.3390/s2407212324:7(2123)Online publication date: 26-Mar-2024
https://doi.org/10.3390/s24072123
Xu GKan E(2024)Device-Free Occupant Counting Using Ambient RFID and Deep Learning2024 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT)10.1109/WiSNeT59910.2024.10438637(49-52)Online publication date: 21-Jan-2024
https://doi.org/10.1109/WiSNeT59910.2024.10438637
Mena-Martinez ADavila Delgado MAlvarado-Uribe JCeballos H(2024)A Real-Life Evaluation of Supervised and Semi-Supervised Machine Learning Approaches for Indirect Estimation of Indoor OccupancyIEEE Access10.1109/ACCESS.2024.344981012(118673-118693)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3449810
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cover image ACM Conferences

BuildSys '16: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments

November 2016

273 pages

ISBN:9781450342643

DOI:10.1145/2993422

Copyright © 2016 ACM.

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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Published: 16 November 2016

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BuildSys '16

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BuildSys '16: The 3rd ACM International Conference on Systems for Energy-Efficient Built Environments

November 16 - 17, 2016

CA, Palo Alto, USA

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Overall Acceptance Rate 148 of 500 submissions, 30%

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Cited By

Chaudhari PXiao YCheng MLi T(2024)Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT SensorsSensors10.3390/s2407212324:7(2123)Online publication date: 26-Mar-2024
https://doi.org/10.3390/s24072123
Xu GKan E(2024)Device-Free Occupant Counting Using Ambient RFID and Deep Learning2024 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT)10.1109/WiSNeT59910.2024.10438637(49-52)Online publication date: 21-Jan-2024
https://doi.org/10.1109/WiSNeT59910.2024.10438637
Mena-Martinez ADavila Delgado MAlvarado-Uribe JCeballos H(2024)A Real-Life Evaluation of Supervised and Semi-Supervised Machine Learning Approaches for Indirect Estimation of Indoor OccupancyIEEE Access10.1109/ACCESS.2024.344981012(118673-118693)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3449810
Ji WYang LLiu ZFeng S(2023)A Systematic Review of Sensing Technology in Human-Building Interaction ResearchBuildings10.3390/buildings1303069113:3(691)Online publication date: 6-Mar-2023
https://doi.org/10.3390/buildings13030691
Nikseresht FCampbell B(2023)FTM-Sense: Robust Sensor-free Occupancy Sensing Leveraging WiFi Fine Time MeasurementProceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3600100.3623741(140-148)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.1145/3600100.3623741
Boovaraghavan SChen CMaravi ACzapik MZhang YHarrison CAgarwal Y(2023)MitesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808657:1(1-32)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580865
Monti LTse RTang SMirri SDelnevo GManiezzo VSalomoni P(2022)Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus ContextSensors10.3390/s2210369222:10(3692)Online publication date: 12-May-2022
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Hasan RHasan R(2022)X-Fidence: Post-Pandemic Wellness By Density Monitoring with Privacy Preservation2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)10.1109/CCNC49033.2022.9700586(578-583)Online publication date: 8-Jan-2022
https://doi.org/10.1109/CCNC49033.2022.9700586
Chu YMitra DO’neill ZCetin K(2021)Influential variables impacting the reliability of building occupancy sensor systems: A systematic review and expert surveyScience and Technology for the Built Environment10.1080/23744731.2021.199367228:2(200-220)Online publication date: 3-Nov-2021
https://doi.org/10.1080/23744731.2021.1993672
Soltanaghaei ESharma RWang ZChittilappilly ALuong AGiler EHall KElias SRowe A(2020)Robust and Practical WiFi Human Sensing Using On-device Learning with a Domain Adaptive ModelProceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3408308.3427983(150-159)Online publication date: 18-Nov-2020
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