Research progress on intelligent assessment system for upper limb function of stroke patients_Journal of Biomedical Engineering

Authors：

LI Sujiao ^1,2,3 , WU Kun ^1,2 , MENG Qiaoling ^1,2,3 ,  YU Hongliu ^1,2,3

1. Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, P. R. China;
3. Key Laboratory of Neural-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai 200093, P. R. China;

Corresponding?author：

Keywords：

Stroke; Upper limb; Function assessment; Intelligent evaluation; Machine learning

DOI：

10.7507/1001-5515.202112046

Video：

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Abstract Full text Figures/Tables Video References Cited by

At present, the upper limb function of stroke patients is often assessed clinically using a scale method, but this method has problems such as time-consuming, poor consistency of assessment results, and high participation of rehabilitation physicians. To overcome the shortcomings of the scale method, intelligent upper limb function assessment systems combining sensors and machine learning algorithms have become one of the hot research topics in recent years. Firstly, the commonly used clinical upper limb functional assessment methods are analyzed and summarized. Then the researches on intelligent assessment systems in recent years are reviewed, focusing on the technologies used in the data acquisition and data processing parts of intelligent assessment systems and their advantages and disadvantages. Lastly, the current challenges and future development directions of intelligent assessment systems are discussed. This review is hoped to provide valuable reference information for researchers in related fields.

Citation： LI Sujiao, WU Kun, MENG Qiaoling, YU Hongliu. Research progress on intelligent assessment system for upper limb function of stroke patients. Journal of Biomedical Engineering, 2022, 39(3): 620-626, 632. doi: 10.7507/1001-5515.202112046 Copy

1.	潘鋒. 現代信息技術提升卒中診療與康復管理水平. 中國醫藥導報, 2021, 18(30): 1-3.
2.	王亞楠, 吳思緲, 劉鳴. 中國腦卒中15年變化趨勢和特點. 華西醫學, 2021, 36(6): 803-807.
3.	古楚兒, 彭剛藝, 應文娟, 等. 老年腦梗死患者社會化住院現狀及影響因素分析. 中華護理雜志, 2019, 54(2): 194-198.
4.	李秀玲. 基于患者需求為導向的臨床護理路徑對卒中后偏癱患者主觀感受及生活質量的影響. 護理實踐與研究, 2020, 17(1): 50-51.
5.	魯守銀, 袁魯浩. 康復機器人的人機交互控制技術研究進展. 山東建筑大學學報, 2021, 36(5): 91-102.
6.	劉朗. 基于深度學習的腦卒中上肢運動功能自動評定. 成都: 電子科技大學, 2020.
7.	錢葉葉, 吳毅. 腦卒中后上肢及手運動功能定量評定方法的研究進展. 中華物理醫學與康復雜志, 2019, 41(6): 469-472.
8.	蔣鴻澤. 基于表面肌電和電刺激的腦卒中評估與康復系統設計. 上海: 上海交通大學, 2019.
9.	劉朗, 劉勇國, 李巧勤, 等. 腦卒中患者運動功能自動化評定研究進展. 中國康復理論與實踐, 2020, 26(9): 1028-1032.
10.	Maceira-Elvira P, Popa T, Schmid A C, et al. Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment. J Neuroeng Rehabil, 2019, 16(1): 142.
11.	吳遠, 高強. Brunnstrom六期評估法在腦卒中偏癱康復中的應用價值和局限性. 中國康復醫學雜志, 2021, 36(4): 505-509.
12.	Kim T-L, Hwang S H, Lee W J, et al. The korean version of the Fugl-Meyer assessment: reliability and validity evaluation. Ann Rehabil Med, 2021, 45(2): 83-98.
13.	馬紅梅, 王寶蘭. 腦卒中后手運動功能評定方法的研究進展. 醫學綜述, 2021, 27(14): 2830-2835.
14.	Tang L, Halloran S, Shi J Q, et al. Evaluating upper limb function after stroke using the free-living accelerometer data. Stat Methods Med Res, 2020, 29(11): 3249-3264.
15.	Chen Z J, He C, Gu M H, et al. Kinematic evaluation via inertial measurement unit associated with upper extremity motor function in subacute stroke: a cross-sectional study. J Healthc Eng, 2021: 4071645.
16.	Zhang X, D'arcy R, Chen L, et al. The feasibility of longitudinal upper extremity motor function assessment using EEG. Sensors, 2020, 20(19): 5487.
17.	Riahi N, Vakorin V A, Menon C. Estimating Fugl-Meyer upper extremity motor score from functional-connectivity measures. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(4): 860-868.
18.	Liu Y, Song Q, Li C, et al. Quantitative assessment of motor function for patients with a stroke by an end-effector upper limb rehabilitation robot. Biomed Res Int, 2020: 5425741.
19.	朱吉鴿, 徐國政, 李進飛. 姿態與肌電融合的腦卒中患者上肢運動功能實時評估方法. 中國康復醫學雜志, 2019, 34(12): 1449-1455.
20.	陳少發, 馬強, 趙君豪, 等. 基于上肢運動評分的動作檢測與動作識別的方法研究. 中國康復醫學雜志, 2019, 34(6): 707-710.
21.	Zhang X, D'arcy R, Menon C. Scoring upper-extremity motor function from EEG with artificial neural networks: a preliminary study. J Neural Eng, 2019, 16(3): 036013.
22.	Isakovi? M S, Savi? A M, Konstantinovi? L M, et al. Validation of computerized square-drawing based evaluation of motor function in patients with stroke. Med Eng Phys, 2019, 71: 114-120.
23.	Fang Q, Mahmoud S S, Gu X, et al. A novel multistandard compliant hand function assessment method using an infrared imaging device. IEEE J Biomed Health Inform, 2019, 23(2): 758-765.
24.	Lee S, Lee Y S, Kim J. Automated evaluation of upper-limb motor function impairment using Fugl-Meyer assessment. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(1): 125-134.
25.	Villan-Villan M A, Perez-Rodriguez R, Martin C, et al. Objective motor assessment for personalized rehabilitation of upper extremity in brain injury patients. NeuroRehabilitation, 2018, 42(4): 429-439.
26.	Li Y, Zhang X, Gong Y, et al. Motor function evaluation of hemiplegic upper-extremities using data fusion from wearable inertial and surface EMG sensors. Sensors, 2017, 17(3): 582.
27.	O?a E D, Sánchez-Herrera P, Cuesta-Gómez A, et al. Automatic outcome in manual dexterity assessment using colour segmentation and nearest neighbour classifier. Sensors, 2018, 18(9): 2876.
28.	Liparulo L, Zhang Z, Panella M, et al. A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography. Med Biol Eng Comput, 2017, 55(8): 1367-1378.
29.	麻琛彬, 徐浩然, 李德玉, 等. 穿戴式生理參數監測及其臨床應用研究進展. 生物醫學工程學雜志, 2021, 38(3): 583-593.
30.	?hberg F, B?cklund T, Sundstr?m N, et al. Portable sensors add reliable kinematic measures to the assessment of upper extremity function. Sensors, 2019, 19(5):1241.
31.	李金奕, 林嘉睿, 楊凌輝, 等. 基于小波和長短時記憶的IMU去噪方法. 傳感技術學報, 2020, 33(5): 677-681.
32.	修展, 葛立. 基于變步長LMS算法的IMU信號降噪研究. 遙測遙控, 2021, 42(2): 35-41.
33.	喬文超, 王紅雨, 王鴻東. 基于BP神經網絡的無人機IMU多傳感器冗余的補償算法. 電子測量與儀器學報, 2020, 34(12): 19-28.
34.	胡鳳丹, 蔡華安, 胡德, 等. 表面肌電在臨床康復中的應用進展. 贛南醫學院學報, 2021, 41(7): 740-744.
35.	Gohel V, Mehendale N. Review on electromyography signal acquisition and processing. Biophys Rev, 2020, 12(6): 1361-1367.
36.	徐瑞, 李志才, 王雯婕, 等. 基于肌電的人機交互控制策略及其應用與挑戰. 電子測量與儀器學報, 2020, 34(2): 1-11.
37.	于祥, 趙翠蓮. 基于sEMG和肌肉深度的多通道陣列電極間距研究. 電子測量技術, 2021, 44(11): 16-21.
38.	戴季高, 徐秀林, 吳曦. 雙通道表面肌電信號采集裝置的設計與分析. 生物醫學工程研究, 2021, 40(1): 66-71.
39.	董鵬輝, 柯良軍. 基于圖像的三維重建技術綜述. 無線電通信技術, 2019, 45(2): 115-119.
40.	張洪, 孫春龍, 杜汶勵. Kinect深度傳感器誤差模型研究. 測控技術, 2016, 35(6): 1-5.
41.	葉勤, 劉行, 姚亞會, 等. 便攜式RGB-D傳感器深度測量與建模精度研究. 同濟大學學報:自然科學版, 2019, 47(6): 870-877.
42.	程皓, 周麗麗, 杜寅福. 腦電信號采集與處理研究. 黑龍江科學, 2021, 12(22): 14-17.
43.	李敏, 彭璐, 顏學軍. 腦電相關監測指標在缺血性腦卒中患者預后評估中的研究進展. 醫學綜述, 2020, 26(1): 107-111.
44.	周晶晶, 葉繼倫, 張旭, 等. 腦電信號分析方法及其應用. 中國醫療器械雜志, 2020, 44(2): 122-126.
45.	周曉霞, 遇濤, 張鑫, 等. 腦電圖采集技術現狀及臨床采集基本技能. 中國醫療設備, 2020, 35(8): 153-156.
46.	劉拓, 葉陽陽, 王坤, 等. 運動想象腦電信號分類算法的研究進展. 生物醫學工程學雜志, 2021, 38(5): 995-1002.
47.	楊劍鋒, 喬佩蕊, 李永梅, 等. 機器學習分類問題及算法研究綜述. 統計與決策, 2019, 35(6): 36-40.
48.	張華, 陶立元, 趙一鳴. 隨機森林算法的原理及其在臨床研究中的應用. 中華兒科雜志, 2021, 59(9): 798-798.
49.	季長清, 高志勇, 秦靜, 等. 基于卷積神經網絡的圖像分類算法綜述. 計算機應用, 2022(4): 1044-1049.
50.	王菲. LSTM循環神經網絡的研究進展與應用. 哈爾濱: 黑龍江大學, 2021.
51.	張瀟, 謝青, 牛傳欣, 等. 觸摸屏軟件在腦卒中后上肢運動功能評估中的可行性研究. 神經損傷與功能重建, 2020, 15(6): 364-365, 370.
52.	O?a E D, Balaguer C, Jardón A. Towards a framework for rehabilitation and assessment of upper limb motor function based on Serious Games//2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH) . Austria: IEEE, 2018: 1-7.
53.	Adams R J, Ellington A L, Armstead K, et al. Upper extremity function assessment using a glove orthosis and virtual reality system. OTJR (Thorofare N J), 2019, 39(2): 81-89.

1. 潘鋒. 現代信息技術提升卒中診療與康復管理水平. 中國醫藥導報, 2021, 18(30): 1-3.
2. 王亞楠, 吳思緲, 劉鳴. 中國腦卒中15年變化趨勢和特點. 華西醫學, 2021, 36(6): 803-807.
3. 古楚兒, 彭剛藝, 應文娟, 等. 老年腦梗死患者社會化住院現狀及影響因素分析. 中華護理雜志, 2019, 54(2): 194-198.
4. 李秀玲. 基于患者需求為導向的臨床護理路徑對卒中后偏癱患者主觀感受及生活質量的影響. 護理實踐與研究, 2020, 17(1): 50-51.
5. 魯守銀, 袁魯浩. 康復機器人的人機交互控制技術研究進展. 山東建筑大學學報, 2021, 36(5): 91-102.
6. 劉朗. 基于深度學習的腦卒中上肢運動功能自動評定. 成都: 電子科技大學, 2020.
7. 錢葉葉, 吳毅. 腦卒中后上肢及手運動功能定量評定方法的研究進展. 中華物理醫學與康復雜志, 2019, 41(6): 469-472.
8. 蔣鴻澤. 基于表面肌電和電刺激的腦卒中評估與康復系統設計. 上海: 上海交通大學, 2019.
9. 劉朗, 劉勇國, 李巧勤, 等. 腦卒中患者運動功能自動化評定研究進展. 中國康復理論與實踐, 2020, 26(9): 1028-1032.
10. Maceira-Elvira P, Popa T, Schmid A C, et al. Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment. J Neuroeng Rehabil, 2019, 16(1): 142.
11. 吳遠, 高強. Brunnstrom六期評估法在腦卒中偏癱康復中的應用價值和局限性. 中國康復醫學雜志, 2021, 36(4): 505-509.
12. Kim T-L, Hwang S H, Lee W J, et al. The korean version of the Fugl-Meyer assessment: reliability and validity evaluation. Ann Rehabil Med, 2021, 45(2): 83-98.
13. 馬紅梅, 王寶蘭. 腦卒中后手運動功能評定方法的研究進展. 醫學綜述, 2021, 27(14): 2830-2835.
14. Tang L, Halloran S, Shi J Q, et al. Evaluating upper limb function after stroke using the free-living accelerometer data. Stat Methods Med Res, 2020, 29(11): 3249-3264.
15. Chen Z J, He C, Gu M H, et al. Kinematic evaluation via inertial measurement unit associated with upper extremity motor function in subacute stroke: a cross-sectional study. J Healthc Eng, 2021: 4071645.
16. Zhang X, D'arcy R, Chen L, et al. The feasibility of longitudinal upper extremity motor function assessment using EEG. Sensors, 2020, 20(19): 5487.
17. Riahi N, Vakorin V A, Menon C. Estimating Fugl-Meyer upper extremity motor score from functional-connectivity measures. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(4): 860-868.
18. Liu Y, Song Q, Li C, et al. Quantitative assessment of motor function for patients with a stroke by an end-effector upper limb rehabilitation robot. Biomed Res Int, 2020: 5425741.
19. 朱吉鴿, 徐國政, 李進飛. 姿態與肌電融合的腦卒中患者上肢運動功能實時評估方法. 中國康復醫學雜志, 2019, 34(12): 1449-1455.
20. 陳少發, 馬強, 趙君豪, 等. 基于上肢運動評分的動作檢測與動作識別的方法研究. 中國康復醫學雜志, 2019, 34(6): 707-710.
21. Zhang X, D'arcy R, Menon C. Scoring upper-extremity motor function from EEG with artificial neural networks: a preliminary study. J Neural Eng, 2019, 16(3): 036013.
22. Isakovi? M S, Savi? A M, Konstantinovi? L M, et al. Validation of computerized square-drawing based evaluation of motor function in patients with stroke. Med Eng Phys, 2019, 71: 114-120.
23. Fang Q, Mahmoud S S, Gu X, et al. A novel multistandard compliant hand function assessment method using an infrared imaging device. IEEE J Biomed Health Inform, 2019, 23(2): 758-765.
24. Lee S, Lee Y S, Kim J. Automated evaluation of upper-limb motor function impairment using Fugl-Meyer assessment. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(1): 125-134.
25. Villan-Villan M A, Perez-Rodriguez R, Martin C, et al. Objective motor assessment for personalized rehabilitation of upper extremity in brain injury patients. NeuroRehabilitation, 2018, 42(4): 429-439.
26. Li Y, Zhang X, Gong Y, et al. Motor function evaluation of hemiplegic upper-extremities using data fusion from wearable inertial and surface EMG sensors. Sensors, 2017, 17(3): 582.
27. O?a E D, Sánchez-Herrera P, Cuesta-Gómez A, et al. Automatic outcome in manual dexterity assessment using colour segmentation and nearest neighbour classifier. Sensors, 2018, 18(9): 2876.
28. Liparulo L, Zhang Z, Panella M, et al. A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography. Med Biol Eng Comput, 2017, 55(8): 1367-1378.
29. 麻琛彬, 徐浩然, 李德玉, 等. 穿戴式生理參數監測及其臨床應用研究進展. 生物醫學工程學雜志, 2021, 38(3): 583-593.
30. ?hberg F, B?cklund T, Sundstr?m N, et al. Portable sensors add reliable kinematic measures to the assessment of upper extremity function. Sensors, 2019, 19(5):1241.
31. 李金奕, 林嘉睿, 楊凌輝, 等. 基于小波和長短時記憶的IMU去噪方法. 傳感技術學報, 2020, 33(5): 677-681.
32. 修展, 葛立. 基于變步長LMS算法的IMU信號降噪研究. 遙測遙控, 2021, 42(2): 35-41.
33. 喬文超, 王紅雨, 王鴻東. 基于BP神經網絡的無人機IMU多傳感器冗余的補償算法. 電子測量與儀器學報, 2020, 34(12): 19-28.
34. 胡鳳丹, 蔡華安, 胡德, 等. 表面肌電在臨床康復中的應用進展. 贛南醫學院學報, 2021, 41(7): 740-744.
35. Gohel V, Mehendale N. Review on electromyography signal acquisition and processing. Biophys Rev, 2020, 12(6): 1361-1367.
36. 徐瑞, 李志才, 王雯婕, 等. 基于肌電的人機交互控制策略及其應用與挑戰. 電子測量與儀器學報, 2020, 34(2): 1-11.
37. 于祥, 趙翠蓮. 基于sEMG和肌肉深度的多通道陣列電極間距研究. 電子測量技術, 2021, 44(11): 16-21.
38. 戴季高, 徐秀林, 吳曦. 雙通道表面肌電信號采集裝置的設計與分析. 生物醫學工程研究, 2021, 40(1): 66-71.
39. 董鵬輝, 柯良軍. 基于圖像的三維重建技術綜述. 無線電通信技術, 2019, 45(2): 115-119.
40. 張洪, 孫春龍, 杜汶勵. Kinect深度傳感器誤差模型研究. 測控技術, 2016, 35(6): 1-5.
41. 葉勤, 劉行, 姚亞會, 等. 便攜式RGB-D傳感器深度測量與建模精度研究. 同濟大學學報:自然科學版, 2019, 47(6): 870-877.
42. 程皓, 周麗麗, 杜寅福. 腦電信號采集與處理研究. 黑龍江科學, 2021, 12(22): 14-17.
43. 李敏, 彭璐, 顏學軍. 腦電相關監測指標在缺血性腦卒中患者預后評估中的研究進展. 醫學綜述, 2020, 26(1): 107-111.
44. 周晶晶, 葉繼倫, 張旭, 等. 腦電信號分析方法及其應用. 中國醫療器械雜志, 2020, 44(2): 122-126.
45. 周曉霞, 遇濤, 張鑫, 等. 腦電圖采集技術現狀及臨床采集基本技能. 中國醫療設備, 2020, 35(8): 153-156.
46. 劉拓, 葉陽陽, 王坤, 等. 運動想象腦電信號分類算法的研究進展. 生物醫學工程學雜志, 2021, 38(5): 995-1002.
47. 楊劍鋒, 喬佩蕊, 李永梅, 等. 機器學習分類問題及算法研究綜述. 統計與決策, 2019, 35(6): 36-40.
48. 張華, 陶立元, 趙一鳴. 隨機森林算法的原理及其在臨床研究中的應用. 中華兒科雜志, 2021, 59(9): 798-798.
49. 季長清, 高志勇, 秦靜, 等. 基于卷積神經網絡的圖像分類算法綜述. 計算機應用, 2022(4): 1044-1049.
50. 王菲. LSTM循環神經網絡的研究進展與應用. 哈爾濱: 黑龍江大學, 2021.
51. 張瀟, 謝青, 牛傳欣, 等. 觸摸屏軟件在腦卒中后上肢運動功能評估中的可行性研究. 神經損傷與功能重建, 2020, 15(6): 364-365, 370.
52. O?a E D, Balaguer C, Jardón A. Towards a framework for rehabilitation and assessment of upper limb motor function based on Serious Games//2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH) . Austria: IEEE, 2018: 1-7.
53. Adams R J, Ellington A L, Armstead K, et al. Upper extremity function assessment using a glove orthosis and virtual reality system. OTJR (Thorofare N J), 2019, 39(2): 81-89.

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