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An estimation of wheelchair user's muscle fatigue by accelerometers on smart devices

Published: 07 September 2015 Publication History

Abstract

This paper introduces an automatic road accessibility information collecting system inspired by human action sensing technologies of wheelchair users. The system aims to estimate a road accessibility caused by environmental factors, e.g. curbs and gaps, which directly influence wheelchair bodies, and also physiological factors, e.g. the wheelchair user's fatigue resulted by the environmental factors. We report that wheelchair user's fatigue influences wheelchair's action data sensed by accelerometer mounted on iPod touch. This paper contributes to discovering patterns of accelerations each of wheelchair user's fatigue and non-fatigue by clustering pushing wheel action data.

References

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Davis, R., Ferrara, M., and Byrnes, D. Sports performance series: The competitive wheelchair stroke. Strength & Conditioning Journal 10, 3 (1988), 4--11.
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Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., and Campbell, A. T. A survey of mobile phone sensing. Communications Magazine, IEEE 48, 9 (2010), 140--150.
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MacQueen, J., et al. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol. 1, Oakland, CA, USA. (1967), 281--297.
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Müller, M. Dynamic time warping. Information retrieval for music and motion (2007), 69--84.
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Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics 20 (1987), 53--65.

Cited By

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  • (2022)FBGs Based System for Muscle Effort Monitoring in Wheelchair UsersIEEE Sensors Journal10.1109/JSEN.2022.317788922:13(12886-12893)Online publication date: 1-Jul-2022
  • (2021)Visualizing Road Condition Information by Applying the AutoEncoder to Wheelchair Sensing Data for Road Barrier AssessmentAdvances in Artificial Intelligence10.1007/978-3-030-73113-7_2(13-24)Online publication date: 23-Jul-2021
  • (2019)Weakly Supervised Learning for Evaluating Road Surface Condition from Wheelchair Driving DataInformation10.3390/info1101000211:1(2)Online publication date: 19-Dec-2019
  • Show More Cited By

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  1. An estimation of wheelchair user's muscle fatigue by accelerometers on smart devices

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    Published In

    cover image ACM Conferences
    UbiComp/ISWC'15 Adjunct: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers
    September 2015
    1626 pages
    ISBN:9781450335751
    DOI:10.1145/2800835
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 September 2015

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    Author Tags

    1. accessibility map
    2. falling accident
    3. machine learning
    4. personal sensing
    5. pushing wheel

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    UbiComp '15
    Sponsor:
    • Yahoo! Japan
    • SIGMOBILE
    • FX Palo Alto Laboratory, Inc.
    • ACM
    • Rakuten Institute of Technology
    • Microsoft
    • Bell Labs
    • SIGCHI
    • Panasonic
    • Telefónica
    • ISTC-PC

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

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    Cited By

    View all
    • (2022)FBGs Based System for Muscle Effort Monitoring in Wheelchair UsersIEEE Sensors Journal10.1109/JSEN.2022.317788922:13(12886-12893)Online publication date: 1-Jul-2022
    • (2021)Visualizing Road Condition Information by Applying the AutoEncoder to Wheelchair Sensing Data for Road Barrier AssessmentAdvances in Artificial Intelligence10.1007/978-3-030-73113-7_2(13-24)Online publication date: 23-Jul-2021
    • (2019)Weakly Supervised Learning for Evaluating Road Surface Condition from Wheelchair Driving DataInformation10.3390/info1101000211:1(2)Online publication date: 19-Dec-2019
    • (2016)A Neuro-Fuzzy System for Classifying Fatigue Degree of Wheelchair UserInternet and Distributed Computing Systems10.1007/978-3-319-45940-0_3(22-33)Online publication date: 21-Sep-2016

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