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An Intelligent and Efficient Rehabilitation Status Evaluation Method: A Case Study on Stroke Patients

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Heterogeneous Data Management, Polystores, and Analytics for Healthcare (DMAH 2020, Poly 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12633))

Abstract

Chronic patients' care encounters challenges, including high cost, lack of professionals, and insufficient rehabilitation state evaluation. Computer-supported cooperative work (CSCW), is capable of alleviating these issues, as it allows healthcare physicians (HCP) to quantify the workload and thus to enhance rehabilitation care quality. This study aims to design a deep learning algorithm Pose-AMGRU, a deep learning-based pose recognition algorithm combining Pose-Attention Mechanism and Gated Recurrent Unit (GRU), to monitor the human pose of rehabilitating patients efficiently. It gives instructions for HCP. To further substantiate the acceptance of our computer-supported method, we develop a multi-fusion theoretical model to determine factors that may influence the acceptance of HCP and verify the usefulness of the method above. Experiment results show Pose-AMGRU achieves an accuracy of 98.61% in the KTH dataset and 100% in the rehabilitation action dataset, which outperforms other algorithms. The efficiency running speed of Pose-AMGRU on the GTX1060 graphics card is up to 14.75FPS, which adapts to the home rehabilitation scene. As to acceptance evaluation, we verified the positive relationship between the computer-supported method and acceptance, and our model presents decent generalizability of stroke patients' care at the Second Affiliated Hospital of Zhengzhou University.

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Acknowledgments

We thank all participants who provided thoughtful and constructive comments on our study. We appreciate Xiaoyi Zhang carried on the guidance of grammar problems on our paper. This research was funded by the National Key Research and Development Program of China (No. 2017YFB1401200), the General Project of Humanistic and Social Science Research of the Department of Education of Henan province. (No. 2018-ZZJH-547), and the program of China Scholarship Council (No. 201907040091).

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Authors and Affiliations

  1. Zhengzhou University, Zhengzhou, 450000, Henan, China

    Yao Tong, Hang Yan, Gang Chen & Zhenxiang Zhang

  2. University of Washington, Seattle, WA, 98105, USA

    Yao Tong

  3. Hohai University, Nanjing, 211000, Jiangsu, China

    Xin Li

Authors
  1. Yao Tong
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  2. Hang Yan
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  3. Xin Li
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  4. Gang Chen
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  5. Zhenxiang Zhang
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Corresponding authors

Correspondence to Gang Chen or Zhenxiang Zhang .

Editor information

Editors and Affiliations

  1. Massachusetts Institute of Technology, Lexington, MA, USA

    Vijay Gadepally

  2. Intel Corporation, Portland, OR, USA

    Timothy Mattson

  3. Massachusetts Institute of Technology, Cambridge, MA, USA

    Michael Stonebraker

  4. Massachusetts Institute of Technology, Cambridge, MA, USA

    Tim Kraska

  5. Stony Brook University, Stony Brook, NY, USA

    Fusheng Wang

  6. University of Washington, Seattle, WA, USA

    Gang Luo

  7. Georgia State University, Atlanta, GA, USA

    Jun Kong

  8. Lucerne Unviersity of Applied Sciences, Rotkreuz, Switzerland

    Alevtina Dubovitskaya

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Tong, Y., Yan, H., Li, X., Chen, G., Zhang, Z. (2021). An Intelligent and Efficient Rehabilitation Status Evaluation Method: A Case Study on Stroke Patients. In: Gadepally, V., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2020 2020. Lecture Notes in Computer Science(), vol 12633. Springer, Cham. https://doi.org/10.1007/978-3-030-71055-2_10

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  • DOI: https://doi.org/10.1007/978-3-030-71055-2_10

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