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Gait Analysis for Physical Rehabilitation via Body-Worn Sensors and Multi-information Fusion

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Book cover Advances in Body Area Networks I

Part of the book series: Internet of Things ((ITTCC))

Abstract

How to effectively use wearable sensors for medical rehabilitation is an interdisciplinary research hotspot of control subjects and biomedical engineering. This paper intends to integrate accelerometer, gyroscope and magnetometer to build a low-cost, intelligent and lightweight wearable human gait analysis platform. On account of complexity and polytopes of walking motion characteristics, the key is to solve the existing robustness and adaptability problems of current gait analysis algorithm. This project is starting from the sensor physical properties and human physiology structure, aiming to establish lower limb kinematics model constraint, and solving the applicability problem of the traditional zero velocity update algorithm. Digital filter and error correction of gait parameters could be done with multi-level data fusion algorithm. Preliminary clinical gait experiments results indicated the proposed method has great potential as an auxiliary for medical rehabilitation. The ultimate target is to realize auxiliary diagnosis and exercise rehabilitation plan formulation for patients with abnormal gait.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61473058, and in part by the Fundamental Research Funds for the Central Universities under Grant DUT18RC(4)034. This project was supported by China Postdoctoral Science Foundation under Grant 2017M621132 and 2017M621131. The authors would like to express their thanks to these funding bodies.

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Authors and Affiliations

  1. School of Control Science and Engineering, Dalian University of Technology, Dalian, 116024, China

    Sen Qiu, Zhelong Wang, Hongyu Zhao, Long Liu, Jiaxin Wang & Jie Li

Authors
  1. Sen Qiu
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  2. Zhelong Wang
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  3. Hongyu Zhao
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  4. Long Liu
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  5. Jiaxin Wang
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  6. Jie Li
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Corresponding author

Correspondence to Sen Qiu .

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Editors and Affiliations

  1. DIMES, Cubo 41C, Universita della Calabria, Rende, Cosenza, Italy

    Giancarlo Fortino

  2. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, Liaoning, China

    Zhelong Wang

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Qiu, S., Wang, Z., Zhao, H., Liu, L., Wang, J., Li, J. (2019). Gait Analysis for Physical Rehabilitation via Body-Worn Sensors and Multi-information Fusion. In: Fortino, G., Wang, Z. (eds) Advances in Body Area Networks I. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-02819-0_11

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  • DOI: https://doi.org/10.1007/978-3-030-02819-0_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02818-3

  • Online ISBN: 978-3-030-02819-0

  • eBook Packages: EngineeringEngineering (R0)

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