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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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.	潘锋. 现代信息技术提升卒中诊疗与康复管理水平. 中国医药导报, 2021, 18(30): 1-3.
2.	王亚楠, 吴思缈, 刘鸣. 中国脑卒中15年变化趋势和特点. 华西医学, 2021, 36(6): 803-807.
3.	古楚儿, 彭刚艺, 应文娟, 等. 老年脑梗死患者社会化住院现状及影响因素分析. 中华护理杂志, 2019, 54(2): 194-198.
4.	李秀玲. 基于患者需求为导向的临床护理路径对卒中后偏瘫患者主观感受及生活质量的影响. 护理实践与研究, 2020, 17(1): 50-51.
5.	鲁守银, 袁鲁浩. 康复机器人的人机交互控制技术研究进展. 山东建筑大学学报, 2021, 36(5): 91-102.
6.	刘朗. 基于深度学习的脑卒中上肢运动功能自动评定. 成都: 电子科技大学, 2020.
7.	钱叶叶, 吴毅. 脑卒中后上肢及手运动功能定量评定方法的研究进展. 中华物理医学与康复杂志, 2019, 41(6): 469-472.
8.	蒋鸿泽. 基于表面肌电和电刺激的脑卒中评估与康复系统设计. 上海: 上海交通大学, 2019.
9.	刘朗, 刘勇国, 李巧勤, 等. 脑卒中患者运动功能自动化评定研究进展. 中国康复理论与实践, 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.	吴远, 高强. Brunnstrom六期评估法在脑卒中偏瘫康复中的应用价值和局限性. 中国康复医学杂志, 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.	马红梅, 王宝兰. 脑卒中后手运动功能评定方法的研究进展. 医学综述, 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.	朱吉鸽, 徐国政, 李进飞. 姿态与肌电融合的脑卒中患者上肢运动功能实时评估方法. 中国康复医学杂志, 2019, 34(12): 1449-1455.
20.	陈少发, 马强, 赵君豪, 等. 基于上肢运动评分的动作检测与动作识别的方法研究. 中国康复医学杂志, 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.	Isaković M S, Savić A M, Konstantinović 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.	Oña 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. 穿戴式生理参数监测及其临床应用研究进展. 生物医学工程学杂志, 2021, 38(3): 583-593.
30.	Öhberg F, Bäcklund T, Sundström 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, 杨凌辉, 等. 基于小波和长短时记忆的IMU去噪方法. 传感技术学报, 2020, 33(5): 677-681.
32.	修展, 葛立. 基于变步长LMS算法的IMU信号降噪研究. 遥测遥控, 2021, 42(2): 35-41.
33.	乔文超, 王红雨, 王鸿东. 基于BP神经网络的无人机IMU多传感器冗余的补偿算法. 电子测量与仪器学报, 2020, 34(12): 19-28.
34.	胡凤丹, 蔡华安, 胡德, 等. 表面肌电在临床康复中的应用进展. 赣南医学院学报, 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. 基于肌电的人机交互控制策略及其应用与挑战. 电子测量与仪器学报, 2020, 34(2): 1-11.
37.	于祥, 赵翠莲. 基于sEMG和肌肉深度的多通道阵列电极间距研究. 电子测量技术, 2021, 44(11): 16-21.
38.	戴季高, 徐秀林, 吴曦. 双通道表面肌电信号采集装置的设计与分析. 生物医学工程研究, 2021, 40(1): 66-71.
39.	董鹏辉, 柯良军. 基于图像的三维重建技术综述. 无线电通信技术, 2019, 45(2): 115-119.
40.	张洪, 孙春龙, 杜汶励. Kinect深度传感器误差模型研究. 测控技术, 2016, 35(6): 1-5.
41.	叶勤, 刘行, 姚亚会, 等. 便携式RGB-D传感器深度测量与建模精度研究. 同济大学学报:自然科学版, 2019, 47(6): 870-877.
42.	程皓, 周丽丽, 杜寅福. 脑电信号采集与处理研究. 黑龙江科学, 2021, 12(22): 14-17.
43.	李敏, 彭璐, 颜学军. 脑电相关监测指标在缺血性脑卒中患者预后评估中的研究进展. 医学综述, 2020, 26(1): 107-111.
44.	周晶晶, 叶继伦, 张旭, 等. 脑电信号分析方法及其应用. 中国医疗器械杂志, 2020, 44(2): 122-126.
45.	周晓霞, 遇涛, 张鑫, 等. 脑电图采集技术现状及临床采集基本技能. 中国医疗设备, 2020, 35(8): 153-156.
46.	刘拓, 叶阳阳, 王坤, 等. 运动想象脑电信号分类算法的研究进展. 生物医学工程学杂志, 2021, 38(5): 995-1002.
47.	杨剑锋, 乔佩蕊, 李永梅, 等. 机器学习分类问题及算法研究综述. 统计与决策, 2019, 35(6): 36-40.
48.	张华, 陶立元, 赵一鸣. 随机森林算法的原理及其在临床研究中的应用. 中华儿科杂志, 2021, 59(9): 798-798.
49.	季长清, 高志勇, 秦静, 等. 基于卷积神经网络的图像分类算法综述. 计算机应用, 2022(4): 1044-1049.
50.	王菲. LSTM循环神经网络的研究进展与应用. 哈尔滨: 黑龙江大学, 2021.
51.	张潇, 谢青, 牛传欣, 等. 触摸屏软件在脑卒中后上肢运动功能评估中的可行性研究. 神经损伤与功能重建, 2020, 15(6): 364-365, 370.
52.	Oña 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. 潘锋. 现代信息技术提升卒中诊疗与康复管理水平. 中国医药导报, 2021, 18(30): 1-3.
2. 王亚楠, 吴思缈, 刘鸣. 中国脑卒中15年变化趋势和特点. 华西医学, 2021, 36(6): 803-807.
3. 古楚儿, 彭刚艺, 应文娟, 等. 老年脑梗死患者社会化住院现状及影响因素分析. 中华护理杂志, 2019, 54(2): 194-198.
4. 李秀玲. 基于患者需求为导向的临床护理路径对卒中后偏瘫患者主观感受及生活质量的影响. 护理实践与研究, 2020, 17(1): 50-51.
5. 鲁守银, 袁鲁浩. 康复机器人的人机交互控制技术研究进展. 山东建筑大学学报, 2021, 36(5): 91-102.
6. 刘朗. 基于深度学习的脑卒中上肢运动功能自动评定. 成都: 电子科技大学, 2020.
7. 钱叶叶, 吴毅. 脑卒中后上肢及手运动功能定量评定方法的研究进展. 中华物理医学与康复杂志, 2019, 41(6): 469-472.
8. 蒋鸿泽. 基于表面肌电和电刺激的脑卒中评估与康复系统设计. 上海: 上海交通大学, 2019.
9. 刘朗, 刘勇国, 李巧勤, 等. 脑卒中患者运动功能自动化评定研究进展. 中国康复理论与实践, 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. 吴远, 高强. Brunnstrom六期评估法在脑卒中偏瘫康复中的应用价值和局限性. 中国康复医学杂志, 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. 马红梅, 王宝兰. 脑卒中后手运动功能评定方法的研究进展. 医学综述, 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. 朱吉鸽, 徐国政, 李进飞. 姿态与肌电融合的脑卒中患者上肢运动功能实时评估方法. 中国康复医学杂志, 2019, 34(12): 1449-1455.
20. 陈少发, 马强, 赵君豪, 等. 基于上肢运动评分的动作检测与动作识别的方法研究. 中国康复医学杂志, 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. Isaković M S, Savić A M, Konstantinović 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. Oña 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. 穿戴式生理参数监测及其临床应用研究进展. 生物医学工程学杂志, 2021, 38(3): 583-593.
30. Öhberg F, Bäcklund T, Sundström 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, 杨凌辉, 等. 基于小波和长短时记忆的IMU去噪方法. 传感技术学报, 2020, 33(5): 677-681.
32. 修展, 葛立. 基于变步长LMS算法的IMU信号降噪研究. 遥测遥控, 2021, 42(2): 35-41.
33. 乔文超, 王红雨, 王鸿东. 基于BP神经网络的无人机IMU多传感器冗余的补偿算法. 电子测量与仪器学报, 2020, 34(12): 19-28.
34. 胡凤丹, 蔡华安, 胡德, 等. 表面肌电在临床康复中的应用进展. 赣南医学院学报, 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. 基于肌电的人机交互控制策略及其应用与挑战. 电子测量与仪器学报, 2020, 34(2): 1-11.
37. 于祥, 赵翠莲. 基于sEMG和肌肉深度的多通道阵列电极间距研究. 电子测量技术, 2021, 44(11): 16-21.
38. 戴季高, 徐秀林, 吴曦. 双通道表面肌电信号采集装置的设计与分析. 生物医学工程研究, 2021, 40(1): 66-71.
39. 董鹏辉, 柯良军. 基于图像的三维重建技术综述. 无线电通信技术, 2019, 45(2): 115-119.
40. 张洪, 孙春龙, 杜汶励. Kinect深度传感器误差模型研究. 测控技术, 2016, 35(6): 1-5.
41. 叶勤, 刘行, 姚亚会, 等. 便携式RGB-D传感器深度测量与建模精度研究. 同济大学学报:自然科学版, 2019, 47(6): 870-877.
42. 程皓, 周丽丽, 杜寅福. 脑电信号采集与处理研究. 黑龙江科学, 2021, 12(22): 14-17.
43. 李敏, 彭璐, 颜学军. 脑电相关监测指标在缺血性脑卒中患者预后评估中的研究进展. 医学综述, 2020, 26(1): 107-111.
44. 周晶晶, 叶继伦, 张旭, 等. 脑电信号分析方法及其应用. 中国医疗器械杂志, 2020, 44(2): 122-126.
45. 周晓霞, 遇涛, 张鑫, 等. 脑电图采集技术现状及临床采集基本技能. 中国医疗设备, 2020, 35(8): 153-156.
46. 刘拓, 叶阳阳, 王坤, 等. 运动想象脑电信号分类算法的研究进展. 生物医学工程学杂志, 2021, 38(5): 995-1002.
47. 杨剑锋, 乔佩蕊, 李永梅, 等. 机器学习分类问题及算法研究综述. 统计与决策, 2019, 35(6): 36-40.
48. 张华, 陶立元, 赵一鸣. 随机森林算法的原理及其在临床研究中的应用. 中华儿科杂志, 2021, 59(9): 798-798.
49. 季长清, 高志勇, 秦静, 等. 基于卷积神经网络的图像分类算法综述. 计算机应用, 2022(4): 1044-1049.
50. 王菲. LSTM循环神经网络的研究进展与应用. 哈尔滨: 黑龙江大学, 2021.
51. 张潇, 谢青, 牛传欣, 等. 触摸屏软件在脑卒中后上肢运动功能评估中的可行性研究. 神经损伤与功能重建, 2020, 15(6): 364-365, 370.
52. Oña 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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