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FootprintID: Indoor Pedestrian Identification through Ambient Structural Vibration Sensing

Authors:

Mostafa Mirshekari,

Jonathon Fagert,

Ole J. Mengshoel,

Pei ZhangAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 3

Article No.: 89, Pages 1 - 31

https://doi.org/10.1145/3130954

Published: 11 September 2017 Publication History

Abstract

We present FootprintID, an indoor pedestrian identification system that utilizes footstep-induced structural vibration to infer pedestrian identities for enabling various smart building applications. Previous studies have explored other sensing methods, including vision-, RF-, mobile-, and acoustic-based methods. They often require specific sensing conditions, including line-of-sight, high sensor density, and carrying wearable devices. Vibration-based methods, on the other hand, provide easy-to-install sparse sensing and utilize gait to distinguish different individuals. However, the challenge for these methods is that the signals are sensitive to the gait variations caused by different walking speeds and the floor variations caused by structural heterogeneity.

We present FootprintID, a vibration-based approach that achieves robust pedestrian identification. The system uses vibration sensors to detect footstep-induced vibrations. It then selects vibration signals and classifiers to accommodate sensing variations, taking step location and frequency into account. We utilize the physical insight on how individual step signal changes with walking speeds and introduce an iterative transductive learning algorithm (ITSVM) to achieve robust classification with limited labeled training data. When trained only on the average walking speed and tested on different walking speeds, FootprintID achieves up to 96% accuracy and a 3X improvement in extreme speeds compared to the Support Vector Machine. Furthermore, it achieves up to 90% accuracy (1.5X improvement) in uncontrolled experiments.

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Cited By

Yang YWang WYan WPeng Y(2025)Footstep Localization on Badminton Court with Vibration Signals: A Pilot StudyElectronics10.3390/electronics1402028914:2(289)Online publication date: 13-Jan-2025
https://doi.org/10.3390/electronics14020289
Dong YWu YKim SSchadl KRose JNoh H(2025)Modeling Foot–Floor Interactions during Walking for Normal and Abnormal GaitsJournal of Engineering Mechanics10.1061/JENMDT.EMENG-7639151:1Online publication date: Jan-2025
https://doi.org/10.1061/JENMDT.EMENG-7639
Hu ZRadmehr AZhang YPan SNguyen P(2024)IOTeethProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435168:1(1-29)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643516
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Index Terms

FootprintID: Indoor Pedestrian Identification through Ambient Structural Vibration Sensing
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 3

September 2017

2023 pages

EISSN:2474-9567

DOI:10.1145/3139486

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Copyright © 2017 ACM.

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Publication History

Published: 11 September 2017

Accepted: 01 July 2017

Revised: 01 May 2017

Received: 01 February 2017

Published in IMWUT Volume 1, Issue 3

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Cited By

Yang YWang WYan WPeng Y(2025)Footstep Localization on Badminton Court with Vibration Signals: A Pilot StudyElectronics10.3390/electronics1402028914:2(289)Online publication date: 13-Jan-2025
https://doi.org/10.3390/electronics14020289
Dong YWu YKim SSchadl KRose JNoh H(2025)Modeling Foot–Floor Interactions during Walking for Normal and Abnormal GaitsJournal of Engineering Mechanics10.1061/JENMDT.EMENG-7639151:1Online publication date: Jan-2025
https://doi.org/10.1061/JENMDT.EMENG-7639
Hu ZRadmehr AZhang YPan SNguyen P(2024)IOTeethProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435168:1(1-29)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643516
Appelle ASalvino LLynch J(2024)Pedestrian footstep localization using a deep convolutional network for time difference of arrival estimationNondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII10.1117/12.3009826(27)Online publication date: 9-May-2024
https://doi.org/10.1117/12.3009826
Xu XDeng RZhao GZhang BLiu C(2024)Deep Domain Generalization-Based Indoor Pedestrian Identification Using Footstep-Induced VibrationsIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.335122773(1-8)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3351227
Wu YSong WWang YMa DXu WHassan MHu W(2024)VibMilk: Nonintrusive Milk Spoilage Detection via Smartphone VibrationIEEE Internet of Things Journal10.1109/JIOT.2024.335904911:10(17184-17197)Online publication date: 15-May-2024
https://doi.org/10.1109/JIOT.2024.3359049
Dong YKim SSchadl KHuang PDing WRose JNoh H(2024)In-Home Gait Abnormality Detection Through Footstep-Induced Floor Vibration Sensing and Person-Invariant Contrastive LearningIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.341381528:12(7054-7067)Online publication date: Dec-2024
https://doi.org/10.1109/JBHI.2024.3413815
Chen JWang CLiu Y(2024)Vibration Signal Based Abnormal Gait Detection and RecognitionIEEE Access10.1109/ACCESS.2024.341737712(89845-89855)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3417377
Single MBruhin LNaef AKrack PNef TGerber S(2024)Unobtrusive measurement of gait parameters using seismographs: An observational studyScientific Reports10.1038/s41598-024-64508-414:1Online publication date: 24-Jun-2024
https://doi.org/10.1038/s41598-024-64508-4
Dong YIammarino MLiu JCodling JFagert JMirshekari MLowes LZhang PNoh H(2024)Ambient floor vibration sensing advances the accessibility of functional gait assessments for children with muscular dystrophiesScientific Reports10.1038/s41598-024-60034-514:1Online publication date: 11-May-2024
https://doi.org/10.1038/s41598-024-60034-5
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