research-article

Smart insole: a wearable system for gait analysis

Authors:

Lei He,

Majid SarrafzadehAuthors Info & Claims

PETRA '12: Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments

Article No.: 18, Pages 1 - 4

https://doi.org/10.1145/2413097.2413120

Published: 06 June 2012 Publication History

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Abstract

Gait analysis is an important medical diagnostic process and has many applications in rehabilitation, therapy and exercise training. However, standard human gait analysis has to be performed in a specific gait lab and operated by a medical professional. This traditional method increases the examination cost and decreases the accuracy of the natural gait model. In this paper, we present a novel portable system, called Smart Insole, to address the current issues. Smart Insole integrates low cost sensors and computes important gait features. In this way, patients or users can wear Smart Insole for gait analysis in daily life instead of participating in gait lab experiments for hours. With our proposed portable sensing system and effective feature extraction algorithm, the Smart Insole system enables precise gait analysis. Furthermore, taking advantage of the affordability and mobility of Smart Insole, pervasive gait analysis can be extended to many potential applications such as fall prevention, life behavior analysis and networked wireless health systems.

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Cited By

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Choi WJeong HOh SJung T(2025)Instant gait classification for hip osteoarthritis patients: a non-wearable sensor approach utilizing Pearson correlation, SMAPE, and GMMBiomedical Engineering Letters10.1007/s13534-024-00448-2Online publication date: 9-Jan-2025
https://doi.org/10.1007/s13534-024-00448-2
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
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Tabi CAyena JOtis M(2024)Machine learning-based risk of fall estimation using insole with force sensors while performing a sequence of activities in the TUG testCogent Engineering10.1080/23311916.2024.243251511:1Online publication date: 29-Nov-2024
https://doi.org/10.1080/23311916.2024.2432515
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Index Terms

Smart insole: a wearable system for gait analysis
1. Information systems
  1. Information systems applications

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Published In

PETRA '12: Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments

June 2012

307 pages

ISBN:9781450313001

DOI:10.1145/2413097

Conference Chair:
Fillia Makedon
University of Texas at Arlington

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2012

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Division of Information and Intelligent Systems

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PETRA2012

Sponsor:

U of Tex at Arlington

PETRA2012: The 5th International Conference on PErvasive Technologies Related to Assistive Environments

June 6 - 8, 2012

Crete, Heraklion, Greece

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Cited By

View all

Choi WJeong HOh SJung T(2025)Instant gait classification for hip osteoarthritis patients: a non-wearable sensor approach utilizing Pearson correlation, SMAPE, and GMMBiomedical Engineering Letters10.1007/s13534-024-00448-2Online publication date: 9-Jan-2025
https://doi.org/10.1007/s13534-024-00448-2
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
Tabi CAyena JOtis M(2024)Machine learning-based risk of fall estimation using insole with force sensors while performing a sequence of activities in the TUG testCogent Engineering10.1080/23311916.2024.243251511:1Online publication date: 29-Nov-2024
https://doi.org/10.1080/23311916.2024.2432515
Gothard AHott JAnton S(2023)Dynamic Characterization of a Low-Cost Fully and Continuously 3D Printed Capacitive Pressure-Sensing System for Plantar Pressure MeasurementsSensors10.3390/s2319820923:19(8209)Online publication date: 30-Sep-2023
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Cho H(2023)Walking Speed Estimation and Gait Classification Using Plantar Pressure and On-Device Deep LearningIEEE Sensors Journal10.1109/JSEN.2023.330502423:19(23336-23347)Online publication date: 1-Oct-2023
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Panahi MMasihi SHanson ARodriguez-Labra JMasihi AMaddipatla DNarakathu BLawson DAtashbar M(2023) Development of a Flexible Smart Wearable Oximeter Insole for Monitoring SpO 2 Levels of Diabetics’ Foot Ulcer IEEE Journal on Flexible Electronics10.1109/JFLEX.2022.32324652:2(61-70)Online publication date: Mar-2023
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