Quantitative assessment of stroke patients based on dynamic time warping algorithm_Journal of Biomedical Engineering

Authors：

LI Shuna ¹ ,  YU Lei ^2,3

1. School of Information and Communication Engineering, North University of China, Taiyuan 030051, P.R.China;
2. Complex Systems Research Center, Shanxi University, Taiyuan 030006, P.R.China;
3. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P.R.China;

Corresponding author：

YU Lei, Email: yulei@sxu.edu.cn

Keywords：

dynamic time warping; accelerometer sensor; stroke; Fugl-Meyer Assessment scale

DOI：

10.7507/1001-5515.201611050

Video：

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In order to accurately evaluate the similarity of motions during daily rehabilitation training for stroke patients, this paper proposed a novel quantitative assessment method based on dynamic time warping (DTW) algorithm. Firstly, the raw accelerometer signals were preprocessed to eliminate the noise. Secondly, the similarity between the accelerometer signals and four standard task templates was calculated respectively, and then the motion was recognized based on the similarity measurements. Finally, the corresponding quantitative assessment model was used to compute the result. The clinical experimental results showed that there were significant differences in the shortest path distance (R value) of DTW between different tasks, and the classification accuracy could be up to 91% when the R value was selected as the classification feature. Additionally, with the process of rehabilitation, the R value decreased gradually, which means that the R value can be taken as the assessment index to evaluate the quality of designated tasks for stroke patients. It also indicated that the R value could be applied into the scene of automatic prescription generation and interactive gaming to determine whether it is needed to change the rehabilitation plan or adjust the game difficulty level, so as to implement the individualized rehabilitation services.

Citation： LI Shuna, YU Lei. Quantitative assessment of stroke patients based on dynamic time warping algorithm. Journal of Biomedical Engineering, 2018, 35(1): 139-144. doi: 10.7507/1001-5515.201611050 Copy

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3.	王玉龙. 康复功能评定学. 北京: 人民卫生出版社, 2008: 438-468.
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6.	Mubashir M, Shao Ling, Seed L. A survey on fall detection: Principles and approaches. Neurocomputing, 2013, 100(SI): 144-152.
7.	汪丰, 邹亚, 乔子晏, 等. 帕金森患者步行运动的定量分析. 东南大学学报: 自然科学版, 2015, 45(2): 266-269.
8.	Baig M M, Gholamhosseini H, Connolly M J. A comprehensive survey of wearable and wireless ECG monitoring systems for older adults. Med Biol Eng Comput, 2013, 51(5): 485-495.
9.	Lara O D, Labrador M A. A survey on human activity recognition using wearable sensors. IEEE Communications Surveys and Tutorials, 2013, 15(3): 1192-1209.
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11.	廖梦佳, 秦亚杰, 汪源源, 等. 基于多传感器融合的用于脑卒中患者的可穿戴式上肢动作识别系统. 中国康复医学杂志, 2015, 30(5): 443-446, 453.
12.	王景丽, 李亮, 郁磊, 等. 基于遗传算法和极限学习机的 Fugl-Meyer 量表自动评估. 计算机应用, 2014, 34(3): 907-910, 914.
13.	Tormene P, Giorgino T, Quaglini S, et al. Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation. Artif Intell Med, 2009, 45(1): 11-34.

1. Liu L, Wang D, Wong K S, et al. Stroke and stroke care in China: huge burden, significant workload, and a national priority. Stroke, 2011, 42(12): 3651-3654.
2. 黄佳, 陈洪沛, 郭敏, 等. 脑卒中患者运动功能评定的方法及其研究进展与问题. 中国临床康复, 2006, 10(28): 120-122.
3. 王玉龙. 康复功能评定学. 北京: 人民卫生出版社, 2008: 438-468.
4. Case M A, Burwick H A, Volpp K G, et al. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA, 2015, 313(6): 625-626.
5. Patel S, Park H, Bonato P, et al. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil, 2012, 9(12): 1-17.
6. Mubashir M, Shao Ling, Seed L. A survey on fall detection: Principles and approaches. Neurocomputing, 2013, 100(SI): 144-152.
7. 汪丰, 邹亚, 乔子晏, 等. 帕金森患者步行运动的定量分析. 东南大学学报: 自然科学版, 2015, 45(2): 266-269.
8. Baig M M, Gholamhosseini H, Connolly M J. A comprehensive survey of wearable and wireless ECG monitoring systems for older adults. Med Biol Eng Comput, 2013, 51(5): 485-495.
9. Lara O D, Labrador M A. A survey on human activity recognition using wearable sensors. IEEE Communications Surveys and Tutorials, 2013, 15(3): 1192-1209.
10. Lin C W, Yang Y T C, Wang J S, et al. A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation. IEEE Trans Inf Technol Biomed, 2012, 16(5, SI): 991-998.
11. 廖梦佳, 秦亚杰, 汪源源, 等. 基于多传感器融合的用于脑卒中患者的可穿戴式上肢动作识别系统. 中国康复医学杂志, 2015, 30(5): 443-446, 453.
12. 王景丽, 李亮, 郁磊, 等. 基于遗传算法和极限学习机的 Fugl-Meyer 量表自动评估. 计算机应用, 2014, 34(3): 907-910, 914.
13. Tormene P, Giorgino T, Quaglini S, et al. Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation. Artif Intell Med, 2009, 45(1): 11-34.

Journal of Biomedical Engineering

Quantitative assessment of stroke patients based on dynamic time warping algorithm

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