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Towards Body Sensor Network Based Gait Abnormality Evaluation for Stroke Survivors

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Body Area Networks: Smart IoT and Big Data for Intelligent Health Management (BODYNETS 2019)

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Abstract

Due to the technological advances of micro-electro-mechanical sensor and wireless sensor network, gait analysis has been widely adopted as an significant indicator of mobility impairment for stroke survivors. This paper aims to propose an wearable computing based gait impairment evaluation method with distribute inertial sensor unit (IMU) mounted on human lower limbs. Temporal-spacial gait metrics were evaluated on more than twenty post stroke patients and ten healthy control subjects in the 10-meters-walk-test. Experimental results shown that significant differences exist between stroke patients and healthy subject in terms of various gait metrics. The extracted gait metrics are consistent with clinical observations, and the position estimation accuracy has been validated by optical device. The proposed method has the potential to serve as an objective and cost-efficient tool for rehabilitation-assisting therapy for post stroke survivors in clinical practice.

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Acknowledgments

This research was funded by National Natural Science Foundation of China (61803072, 61873044 and 61903062), China Postdoctoral Science Foundation (2017M621131 and 2017M621132), Liaoning Natural Science Foundation Key Project no. 20180540011, Dalian Science and Technology Innovation fund (2019J13SN99 and 2018J12SN077), Fundamental Research Funds for the Central Universities no. DUT18RC(4)034, and National Defence Pre-research Foundation no. 614250607011708.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, 116024, China

    Sen Qiu, Hongyu Zhao & Zhelong Wang

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

    Sen Qiu, Xiangyang Guo, Hongyu Zhao & Zhelong Wang

  3. Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Via P. Bucci, 87036, Rende, CS, Italy

    Qimeng Li & Raffaele Gravina

Authors
  1. Sen Qiu
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  2. Xiangyang Guo
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  3. Hongyu Zhao
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  4. Zhelong Wang
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  5. Qimeng Li
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  6. Raffaele Gravina
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Corresponding author

Correspondence to Sen Qiu .

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Editors and Affiliations

  1. Department of Information Engineering, University of Florence, Florence, Italy

    Lorenzo Mucchi

  2. Centre for Wireless Communications, University of Oulu, Oulu, Finland

    Matti Hämäläinen

  3. University of Florence, Florence, Italy

    Sara Jayousi

  4. University of Florence, Florence, Italy

    Simone Morosi

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Qiu, S., Guo, X., Zhao, H., Wang, Z., Li, Q., Gravina, R. (2019). Towards Body Sensor Network Based Gait Abnormality Evaluation for Stroke Survivors. In: Mucchi, L., Hämäläinen, M., Jayousi, S., Morosi, S. (eds) Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-030-34833-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-34833-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34832-8

  • Online ISBN: 978-3-030-34833-5

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