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SonicDoor: A Person Identification System Based on Modeling of Shape, Behavior, and Walking Patterns

Published: 08 December 2018 Publication History

Abstract

Non-intrusive occupant identification enables numerous applications in Smart Buildings such as personalization of climate and lighting. Current techniques do not scale beyond 20 people, whereas commercial buildings have 100 or more people. This article proposes a new method to identify occupants by sensing their body shape, movement, and walking patterns as they walk through a SonicDoor, a door instrumented with three ultrasonic sensors. The proposed method infers contextual information, such as paths and historical walks through different doors of the building. Each SonicDoor is instrumented with ultrasonic ping sensors, one on top sensing height and two on the sides of the door sensing width of the person walking through the door. SonicDoor detects a walking event and analyzes it to infer whether the Walker is using a phone, holding a handbag, or wearing a backpack. It extracts a set of features from the walking event and corrects them using a set of transformation functions to mitigate the bias. We deployed five SonicDoors in a real building for two months and collected data consisting of over 9,000 walking events spanning over 170 people. The proposed method identifies 100 occupants with an accuracy of 90.2%, which makes it suitable for commercial buildings.

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Published In

cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 14, Issue 3-4
Special Issue on BuildSys'17
November 2018
392 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/3294070
Issue’s Table of Contents
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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Publication History

Published: 08 December 2018
Accepted: 01 May 2018
Revised: 01 May 2018
Received: 01 January 2018
Published in TOSN Volume 14, Issue 3-4

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Author Tags

  1. Wireless sensor networks
  2. media access control
  3. multi-channel
  4. radio interference
  5. time synchronization

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Cited By

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  • (2023)Multi-User Room-Scale Respiration Tracking Using COTS Acoustic DevicesACM Transactions on Sensor Networks10.1145/359422019:4(1-28)Online publication date: 24-Apr-2023
  • (2022)Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19Computer Systems Science and Engineering10.32604/csse.2022.02050441:1(305-320)Online publication date: 2022
  • (2020)Ontology-Based Modeling of Privacy Vulnerabilities for Data SharingPrivacy and Identity Management. Data for Better Living: AI and Privacy10.1007/978-3-030-42504-3_8(109-125)Online publication date: 6-Mar-2020
  • (2019)Computation Offloading with Multiple Agents in Edge-Computing–Supported IoTACM Transactions on Sensor Networks10.1145/337202516:1(1-27)Online publication date: 19-Dec-2019
  • (2019)Smart home resident identification based on behavioral patterns using ambient sensorsPersonal and Ubiquitous Computing10.1007/s00779-019-01288-zOnline publication date: 16-Aug-2019

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