Current status of cloud rehabilitation of stroke in China_West China Medical Journal

Authors：

WANG Tingting ^1,2 ,  QU Yun ^1,2

1. Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Key Laboratory of Rehabilitation Medicine in Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

QU Yun, Email: dr_yunqu@163.com

Keywords：

Stroke; Cloud rehabilitation; Artificial intelligence; Telerehabilitation

DOI：

10.7507/1002-0179.202004377

Video：

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Abstract Full text Figures/Tables Video References Cited by

Stroke is a kind of cerebrovascular disease with high incidence and disability rate. Motor dysfunction and cognitive dysfunction are common dysfunctions of stroke. Rehabilitation treatment can effectively reduce the disability rate of stroke and improve the quality of life. The short-term hospitalization and ambulatory rehabilitation treatment cannot meet the rehabilitation needs of stroke patients. Cloud rehabilitation is one of the ways to solve this problem. This article introduces the definition and application of cloud rehabilitation and artificial intelligence (including assisted rehabilitation assessment and assisted rehabilitation treatment), and summarizes the current problems in the development of stroke cloud rehabilitation in China, so as to promote the construction of remote rehabilitation based on artificial intelligence in China and provide some references for the selection of rehabilitation programs for patients with stroke.

Citation： WANG Tingting, QU Yun. Current status of cloud rehabilitation of stroke in China. West China Medical Journal, 2020, 35(6): 652-657. doi: 10.7507/1002-0179.202004377 Copy

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10. Feng W, Hendry RM, Adams RJ. Risk of recurrent stroke, myocardial infarction, or death in hospitalized stroke patients. Neurology, 2010, 74(7): 588-593.
11. Wang W, Jiang B, Sun H, et al. Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults. Circulation, 2017, 135(8): 759-771.
12. Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation. Lancet, 2011, 377(9778): 1693-1702.
13. Donnelly M, Power M, Russell M, et al. Randomized controlled trial of an early discharge rehabilitation service: the Belfast community stroke trial. Stroke, 2003, 35(1): 127-133.
14. Andrew NE, Kilkenny M, Naylor R, et al. Understanding long-term unmet needs in Australian survivors of stroke. Int J Stroke, 2014, 9(Suppl A100): 106-112.
15. Kong KH, Lee J. Temporal recovery of activities of daily living in the first year after ischemic stroke: a prospective study of patients admitted to a rehabilitation unit. NeuroRehabilitation, 2014, 35(2): 221-226.
16. 王静静, 马睿, 屈云. 脑卒中下肢远程康复技术研究进展. 中国医疗器械杂志, 2019, 43(3): 188-191.
17. 马睿, 屈云, 王婷婷, 等. 远程康复技术在记忆障碍中的应用研究进展. 中国康复, 2019, 34(8): 437-440.
18. Reinkensmeyer DJ, Pang CT, Nessler JA, et al. Web-based telerehabilitation for the upper extremity after stroke. IEEE Trans Neural Syst Rehabil Eng, 2002, 10(2): 102-108.
19. Rodriquez-de-Pablo C, Perry JC, Cavallaro FI, et al. Development of computer games for assessment and training in post-stroke arm telerehabilitation. Conf Proc IEEE Eng Med Biol Soc, 2012, 2012: 4571-4574.
20. Jamwal PK, Hussain S, Mir-Nasiri N, et al. Tele-rehabilitation using in-house wearable ankle rehabilitation robot. Assist Technol, 2016, 22: 1-10.
21. Brennan DM, Mawson S, Brownsell S. Telerehabilitation: enabling the remote delivery of healthcare, rehabilitation, and self management. Stud Health Technol Inform, 2009, 145: 231-248.
22. 夏敏燕. 云康复管理系统下的康复理疗床系列产品设计. 包装工程, 2019, 40(20): 109-113.
23. 周钰, 陈卓铭, 关汉添, 等. 精准璟云平台认知训练治疗认知障碍合并失语症病例报告. 康复学报, 2019, 29(4): 54-60.
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25. 郭琳琳, 苏鹏鹏, 杨三华, 等. 云技术应用于教育康复专业教学的思考—基于康复云的教学案例. 绥化学院学报, 2018, 38(7): 139-141.
26. 张玉红, 黄昭鸣, 刘巧云. 特殊教育专业康复实践教学的运行困境与突围路径—基于智慧康复云服务的视角. 中国特殊教育, 2015(11): 49-55.
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30. 孟琳, 都天慧, 范晶晶, 等. 基于微型传感器的可穿戴远程康复设备的设计. 中国医疗器械杂志, 2017, 41(3): 189-192.
31. 都天慧, 袁梦玮, 屈云. 基于安全性和用户体验的远程康复系统设计. 中国医疗器械杂志, 2017, 41(2): 110-113.
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33. 宋煜. 人工智能视域下健康大数据的应用实践研究. 中国发展, 2017, 17(6): 76-81.
34. 倪俊瑜. 下肢康复训练机器人. 中国伤残医学, 2010, 19(1): 127-128.
35. Wolf SL, Sahu K, Bay RC, et al. The HAAPI (Home Arm Assistance Progression Initiative) trial. Neurorehabil Neural Repair, 2015, 29(10): 958-968.
36. 范虹, 吴月峰, 董晓琼, 等. 上肢康复机器人对急性期脑卒中患者上肢运动功能恢复的影响. 中华物理医学与康复杂志, 2016, 38(2): 104-107.
37. 何斌, 张超, 刘璇. 上肢机器人辅助疗法对急性期脑卒中患者上肢运动功能的效果. 中国康复理论与实践, 2016, 22(6): 688-692.
38. 张超, 刘璇, 侯增广, 等. 上肢机器人辅助疗法对恢复期脑卒中患者上肢运动功能及日常生活活动能力的效果. 中国康复理论与实践, 2016, 22(12): 1365-1370.
39. 王秋纯. 上肢机器人和徒手治疗对脑卒中上肢功能康复的疗效观察. 广州: 广州中医药大学, 2016: 7-8.
40. 王会才, 赵凯, 葛玥. 康复机器人训练对脑卒中偏瘫上肢功能及神经电生理的影响. 安徽医药, 2014, 18(9): 1690-1694.
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