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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))

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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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References

  1. Enthoven, A.C., Tollen, L.A.: Competition in health care: it takes systems to pursue quality and efficiency. Health Aff. (Millwood). Suppl Web 420–433 (2005)

    Google Scholar 

  2. Owens, G.M.: Gender differences in health care expenditures, resource utilization, and quality of care. J. Manag. Care Pharm. 14, 2–6 (2008)

    Article  Google Scholar 

  3. Parekh, A.K., Goodman, R.A., Gordon, C., Koh, H.K.: Managing multiple chronic conditions: a strategic framework for improving health outcomes and quality of life. Public Health Rep. 126, 460–471 (2011)

    Article  Google Scholar 

  4. Wennberg, J.E., Fisher, E.S., Baker, L., Sharp, S.M., Bronner, K.K.: Evaluating the efficiency of california providers in caring for patients with chronic illnesses. Health Aff. (Millwood). Suppl Web 526–543 (2005)

    Google Scholar 

  5. Kischka, U., Wade, D.T.: Rehabilitation after stroke. Handb. Cerebrovasc. Dis. Second Ed. Revis. Expand. 231–241 (2004)

    Google Scholar 

  6. Jørgensen, H.S., Nakayama, H., Raaschou, H.O., Vive-Larsen, J., Støier, M., Olsen, T.S.: Outcome and time course of recovery in stroke. Part I: outcome. The Copenhagen stroke study. Arch. Phys. Med. Rehabil. 76, 399–405 (1995)

    Article  Google Scholar 

  7. Teasell, R.W., McRae, M.P., Finestone, H.M.: Social issues in the rehabilitation of younger stroke patients. Arch. Phys. Med. Rehabil. 81, 205–209 (2000)

    Article  Google Scholar 

  8. Enthoven, A.C., Vorhaus, C.B.: A vision of quality in health care delivery. Health Aff. 16, 44–57 (1997)

    Article  Google Scholar 

  9. Feder, J., Komisar, H.L., Niefeld, M.: Long-term care in the United States: an overview. Health Aff. 19, 40–56 (2000). https://doi.org/10.1377/hlthaff.19.3.40

  10. LAST: Role of Effective Teamwork and Communication in Delivering Safe, High-Quality Care. Medicine (Baltimore), pp. 15–21 (2007)

    Google Scholar 

  11. Waters, T.M., Kaplan, C.M., Graetz, I., Price, M.M., Stevens, L.A., McAneny, B.L.: Patient-centered medical homes in community oncology practices: changes in spending and care quality associated with the COME HOME experience. J. Oncol. Pract. 15, e56–e64 (2019)

    Article  Google Scholar 

  12. Feo, R., Rasmussen, P., Wiechula, R., Conroy, T., Kitson, A.: Developing effective and caring nurse-patient relationships. Nurs. Stand. 31, 54–63 (2017)

    Article  Google Scholar 

  13. Lee, W., Park, J., Park, C.H.: Acceptability of tele-assistive robotic nurse for human-robot collaboration in medical environment, pp. 171–172 (2018)

    Google Scholar 

  14. Wang, Z.W., An, Y.: The analysis on the construction of CSCW system and group collaborative mode. Adv. Mater. Res. 756–759, 2966–2970 (2013)

    Article  Google Scholar 

  15. Stahl, G.: Theories of collaborative cognition: foundations for CSCL and CSCW together. In: Goggins, S., Jahnke, I., Wulf, V. (eds.) Computer-Supported Collaborative Learning at the Workplace. Computer-Supported Collaborative Learning Series, vol. 14, pp. 43–63. Springer, Boston (2013). https://doi.org/10.1007/978-1-4614-1740-8_3

  16. Reddy, M.C., Bardram, J., Gorman, P.: CSCW research in healthcare: past, present, and future. Work. 615–616 (2010)

    Google Scholar 

  17. Hood, M., Wilson, R., Corsica, J., Bradley, L., Chirinos, D., Vivo, A.: What do we know about mobile applications for diabetes self-management? A review of reviews. J. Behav. Med. 39(6), 981–994 (2016). https://doi.org/10.1007/s10865-016-9765-3

    Article  Google Scholar 

  18. Kowitlawakul, Y.: The technology acceptance model: predicting nurses’ intention to use telemedicine technology (eICU). CIN - Comput. Informatics Nurs. 29, 411–418 (2011)

    Article  Google Scholar 

  19. Moon, B.C., Chang, H.: Technology acceptance and adoption of innovative smartphone uses among hospital employees. Healthc. Inform. Res. 20, 304–312 (2014)

    Article  Google Scholar 

  20. Matthias, M.S., et al.: Self-management support and communication from nurse care managers compared with primary care physicians: a focus group study of patients with chronic musculoskeletal pain. Pain Manag. Nurs. 11, 26–34 (2010)

    Article  Google Scholar 

  21. Dou, K., et al.: Patients’ acceptance of smartphone health technology for chronic disease management: a theoretical model and empirical test. JMIR mHealth uHealth. 5, e177 (2017)

    Article  Google Scholar 

  22. Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. Proc. - Int. Conf. Pattern Recognit. 3, 32–36 (2004)

    Article  Google Scholar 

  23. Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 1302–1310 (2017)

    Google Scholar 

  24. Rowe, N.C., Chan, A.L.: On the properties of neural machine translation: encoder–decoder approaches. In: Proceedings of 2011 International Conference on Image Processing, Computer Vision, Pattern Recognition, IPCV 2011, vol. 1, pp. 317–322 (2011)

    Google Scholar 

  25. Liu, W., et al.: SSD: Single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  26. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua, 2261–2269 (2017).

    Google Scholar 

  27. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manage. Sci. 35, 982–1003 (1989). https://www.jstor.org/stable/2632151

  28. Yarbrough, A.K., Smith, T.B.: A new take on TAM. Med. Care Res. Rev. 64, 650–672 (2007)

    Article  Google Scholar 

  29. Ooi, K.B., Tan, G.W.H.: Mobile technology acceptance model: an investigation using mobile users to explore smartphone credit card. Expert Syst. Appl. 59, 33–46 (2016)

    Article  Google Scholar 

  30. Müller, J.M.: Comparing technology acceptance for autonomous vehicles, battery electric vehicles, and car sharing-a study across Europe, China, and North America. Sustain. 11, 4333 (2019)

    Article  Google Scholar 

  31. Hu, P.J., Chau, P.Y.K., Sheng, O.R.L., Tam, K.Y.: Examining acceptance model using physician of acceptance telemedicine technology. J. Manag. Inf. Syst. 16, 91–112 (2012)

    Article  Google Scholar 

  32. Park, Y., Chen, J.V.: Acceptance and adoption of the innovative use of smartphone. Ind. Manag. Data Syst. 107, 1349–1365 (2007)

    Article  Google Scholar 

  33. Lee, Y., Kozar, K.A., Larsen, K.R.T.: The technology acceptance model: past, present, and future. Commun. Assoc. Inf. Syst. 12, 50 (2003)

    Google Scholar 

  34. Chau, P.Y.K., Hu, P.J.-H.: Information technology acceptance by individual professionals: a model comparison approach. Decis. Sci. 32, 699–719 (2007)

    Article  Google Scholar 

  35. Godin, G., Bélanger-Gravel, A., Eccles, M., Grimshaw, J.: Healthcare professionals’ intentions and behaviours: a systematic review of studies based on social cognitive theories. Implement. Sci. 3, 1–2 (2008)

    Article  Google Scholar 

  36. Venkatesh, M., Davis, D.: User acceptance of information technology: toward a unified view. MIS Q. 27, 425 (2003). https://doi.org/10.2307/30036540

  37. Jewer, J.: Patients’ intention to use online postings of ED wait times: a modified UTAUT model. Int. J. Med. Inform. 112, 34–39 (2018)

    Google Scholar 

  38. Krishnan, G., Mintz, J., Foreman, A., Hodge, J.C., Krishnan, S.: The acceptance and adoption of transoral robotic surgery in Australia and New Zealand. J. Robot. Surg. 13(2), 301–307 (2018). https://doi.org/10.1007/s11701-018-0856-8

    Article  Google Scholar 

  39. Cimperman, M., Makovec Brenčič, M., Trkman, P.: Analyzing older users’ home telehealth services acceptance behavior-applying an Extended UTAUT model. Int. J. Med. Inform. 90, 22–31 (2016)

    Article  Google Scholar 

  40. Ramirez, M., Wu, S., Ryan, G., Towfighi, A., Vickrey, B.G.: Using beta-version mhealth technology for team-based care management to support stroke prevention: an assessment of utility and challenges. JMIR Res. Protoc. 6, e94 (2017)

    Article  Google Scholar 

  41. Carayon, P., et al.: Work system design for patient safety: the SEIPS model. Qual. Saf. Heal. Care. 15, 50–58 (2006)

    Article  Google Scholar 

  42. Dul, J., et al.: A strategy for human factors/ergonomics: developing the discipline and profession. Ergonomics 55, 377–395 (2012)

    Article  Google Scholar 

  43. Rublee, D.A.: The quality of care: how can it be assessed? JAMA J. Am. Med. Assoc. 261, 1151 (1989)

    Article  Google Scholar 

  44. Wooldridge, A.R., Carayon, P., Hundt, A.S., Hoonakker, P.L.T.: SEIPS-based process modeling in primary care. Appl. Ergon. 60, 240–254 (2017)

    Article  Google Scholar 

  45. Zhou, M., Zhao, L., Kong, N., Campy, K.S., Qu, S., Wang, S.: Factors influencing behavior intentions to telehealth by Chinese elderly: an extended TAM model. Int. J. Med. Inform. 126, 118–127 (2019)

    Article  Google Scholar 

  46. Yu, P., Li, H., Gagnon, M.P.: Health IT acceptance factors in long-term care facilities: a cross-sectional survey. Int. J. Med. Inform. 78, 219–229 (2009)

    Article  Google Scholar 

  47. Carayon, P., et al.: Human factors systems approach to healthcare quality and patient safety. Appl. Ergon. 45, 14–25 (2014)

    Article  Google Scholar 

  48. Razmak, J., Bélanger, C.H., Farhan, W.: Development of a techno-humanist model for e-health adoption of innovative technology. Int. J. Med. Inform. 120, 62–76 (2018)

    Article  Google Scholar 

  49. Todd, P.A., Taylor, S.: Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6, 144–176 (1995). https://www.jstor.org/stable/23011007. Understanding Models Usage

  50. Schoot, T., Zuyd, H.: Client-centred care balancing between perspectives (2015)

    Google Scholar 

  51. Ji, S., Xu, W., Yang, M., Yu, K.: 3D Convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 221–231 (2013)

    Article  Google Scholar 

  52. Megrhi, S., Jmal, M., Souidene, W., Beghdadi, A.: Spatio-temporal action localization and detection for human action recognition in big dataset. J. Vis. Commun. Image Represent. 41, 375–390 (2016)

    Article  Google Scholar 

  53. Yuxi, M., Li, T., Dong, X., et al.: Action recognition for intelligent monitoring. J. Image Graph. 24(02), 128–136 (2019)

    Google Scholar 

  54. Sargano, A.B., Wang, X., Angelov, P., Habib, Z.: Human action recognition using transfer learning with deep representations. In: Proceedings of International Joint Conference on Neural Networks, vol. 2017-May, pp. 463–469 (2017)

    Google Scholar 

  55. Guo, X., Sun, Y., Wang, N., Peng, Z., Yan, Z.: The dark side of elderly acceptance of preventive mobile health services in China. Electron. Mark. 23, 49–61 (2013)

    Article  Google Scholar 

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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

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  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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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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