A two-dimensional video based quantification method and clinical application research of motion disorders_Journal of Biomedical Engineering

Authors：

SUN Yubo ^1,2 , LIU Peipei ³ , YANG Yuchen ^1,2 , YU Yang ⁴ , YU Huan ³ , SUN Xiaoyi ³ ,  WU Jialing ^3,4 , HAN Jianda ^1,2,5,6 ,  YU Ningbo ^1,2,5,6

1. College of Artificial Intelligence, Nankai University, Tianjin 300350, P. R. China;
2. Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, P. R. China;
3. Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, P. R. China;
4. Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin 300350, P. R. China;
5. Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, P. R. China;
6. Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen, Guangdong 518083, P. R. China;

Corresponding author：

WU Jialing, Email: wywjl2009@hotmail.com; YU Ningbo, Email: nyu@nankai.edu.cn

Keywords：

Telemedicine; Motion disorders; Two-dimensional video; Gait features; Machine learning

DOI：

10.7507/1001-5515.202203052

Video：

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The increasing prevalence of the aging population, and inadequate and uneven distribution of medical resources, have led to a growing demand for telemedicine services. Gait disturbance is a primary symptom of neurological disorders such as Parkinson’s disease (PD). This study proposed a novel approach for the quantitative assessment and analysis of gait disturbance from two-dimensional (2D) videos captured using smartphones. The approach used a convolutional pose machine to extract human body joints and a gait phase segmentation algorithm based on node motion characteristics to identify the gait phase. Moreover, it extracted features of the upper and lower limbs. A height ratio-based spatial feature extraction method was proposed that effectively captures spatial information. The proposed method underwent validation via error analysis, correction compensation, and accuracy verification using the motion capture system. Specifically, the proposed method achieved an extracted step length error of less than 3 cm. The proposed method underwent clinical validation, recruiting 64 patients with Parkinson’s disease and 46 healthy controls of the same age group. Various gait indicators were statistically analyzed using three classic classification methods, with the random forest method achieving a classification accuracy of 91%. This method provides an objective, convenient, and intelligent solution for telemedicine focused on movement disorders in neurological diseases.

Citation： SUN Yubo, LIU Peipei, YANG Yuchen, YU Yang, YU Huan, SUN Xiaoyi, WU Jialing, HAN Jianda, YU Ningbo. A two-dimensional video based quantification method and clinical application research of motion disorders. Journal of Biomedical Engineering, 2023, 40(3): 499-507. doi: 10.7507/1001-5515.202203052 Copy

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2.	Vos T, Allen C, Arora M, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet, 2016, 388(10053): 1545-1602.
3.	Liu Y, Chen J, Hu C, et al. Vision-based method for automatic quantification of parkinsonian bradykinesia. IEEE T Neur Sys Reh, 2019, 27(10): 1952-1961.
4.	Yang W, Hamilton J L, Kopil C, et al. Current and projected future economic burden of Parkinson’s disease in the US. NPJ Parkinsons Dis, 2020, 6(1): 15.
5.	Bloem B R, Okun M S, Klein C. Parkinson’s disease. Lancet, 2021, 397(10291): 2284-2303.
6.	Goetz C G, Tilley B C, Shaftman S R, et al. Movement Disorder Society‐sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS): scale presentation and clinimetric testing results. Movement Disord, 2008, 23(15): 2129-2170.
7.	Aich S, Pradhan P M, Park J, et al. A machine learning approach to distinguish Parkinson’s disease (PD) patient’s with shuffling gait from older adults based on gait signals using 3D motion analysis. Int J Eng Technol, 2018, 7(3.29): 153-156.
8.	Slijepcevic D, Zeppelzauer M, Gorgas A M, et al. Automatic classification of functional gait disorders. IEEE J Biomed Health, 2017, 22(5): 1653-1661.
9.	Reither L R, Foreman M H, Migotsky N, et al. Upper extremity movement reliability and validity of the Kinect version 2. Disabil Rehabil-Assi, 2018, 13(1): 54-59.
10.	Zhu W, Anderson B, Zhu S, et al. A computer vision-based system for stride length estimation using a mobile phone camera// Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility. Reno: ACM, 2016: 121-130.
11.	Nardo A, Anasetti F, Servello D, et al. Quantitative gait analysis in patients with Parkinson treated with deep brain stimulation: the effects of a robotic gait training. NeuroRehabilitation, 2014, 35(4): 779-788.
12.	刘俊杰. 基于机器学习与步态分析的帕金森病患者定量评估与分类方法研究. 杭州: 杭州电子科技大学, 2021.
13.	Greene B R, McGrath D, O’Neill R, et al. An adaptive gyroscope-based algorithm for temporal gait analysis. Med Biol Eng Comput, 2010, 48(12): 1251-1260.
14.	Lima A L S, Smits T, Darweesh S K L, et al. Home-based monitoring of falls using wearable sensors in Parkinson’s disease. Movement Disord, 2020, 35(1): 109-115.
15.	沈天毓, 王计平, 郭立泉, 等. 利用可穿戴设备对帕金森病患者运动功能进行量化评估. 生物医学工程学杂志, 2018, 35(2): 206-213.
16.	朱业安, 徐唯祎, 王睿, 等. 偏瘫步态障碍的自动识别和分析. 生物医学工程学杂志, 2019, 36(2): 306-314.
17.	Kim A, Kim J, Rietdyk S, et al. A wearable smartphone-enabled camera-based system for gait assessment. Gait Posture, 2015, 42(2): 138-144.
18.	Gabel M, Gilad-Bachrach R, Renshaw E, et al. Full body gait analysis with Kinect// 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego: IEEE, 2012: 1964-1967.
19.	Verlekar T T, Soares L D, Correia P L. Automatic classification of gait impairments using a markerless 2D video-based system. Sensors, 2018, 18(9): 2743.
20.	Stenum J, Rossi C, Roemmich R T. Two-dimensional video-based analysis of human gait using pose estimation. PLoS Comput Biol, 2021, 17(4): e1008935.
21.	Kanko R M, Laende E K, Strutzenberger G, et al. Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system. J Biomech, 2021, 122: 110414.
22.	Romijnders R, Warmerdam E, Hansen C, et al. Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson’s Disease patients. J Neuroeng Rehabil, 2021, 18(1): 28.
23.	Jakob V, Küderle A, Kluge F, et al. Validation of a sensor-based gait analysis system with a gold-standard motion capture system in patients with Parkinson’s disease. Sensors, 2021, 21(22): 7680.
24.	Veeraragavan S, Gopalai A A, Gouwanda D, et al. Parkinson’s disease diagnosis and severity assessment using ground reaction forces and neural networks. Front Physiol, 2020, 11: 587057.
25.	Balaji E, Brindha D, Balakrishnan R. Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease. Appl Soft Comput, 2020, 94: 106494.
26.	Bouten C V C, Koekkoek K T M, Verduin M, et al. A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE T Bio-Med Eng, 1997, 44(3): 136-147.
27.	Stuberg W A, Colerick V L, Blanke D J, et al. Comparison of a clinical gait analysis method using videography and temporal-distance measures with 16-mm cinematography. Phys Ther, 1988, 68(8): 1221-1225.
28.	Lewek M D, Poole R, Johnson J, et al. Arm swing magnitude and asymmetry during gait in the early stages of Parkinson’s disease. Gait Posture, 2010, 31(2): 256-260.

1. Helmich R C, Bloem B R. The impact of the COVID-19 pandemic on Parkinson’s disease: hidden sorrows and emerging opportunities. J Parkinson Dis, 2020, 10(2): 351-354.
2. Vos T, Allen C, Arora M, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet, 2016, 388(10053): 1545-1602.
3. Liu Y, Chen J, Hu C, et al. Vision-based method for automatic quantification of parkinsonian bradykinesia. IEEE T Neur Sys Reh, 2019, 27(10): 1952-1961.
4. Yang W, Hamilton J L, Kopil C, et al. Current and projected future economic burden of Parkinson’s disease in the US. NPJ Parkinsons Dis, 2020, 6(1): 15.
5. Bloem B R, Okun M S, Klein C. Parkinson’s disease. Lancet, 2021, 397(10291): 2284-2303.
6. Goetz C G, Tilley B C, Shaftman S R, et al. Movement Disorder Society‐sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS‐UPDRS): scale presentation and clinimetric testing results. Movement Disord, 2008, 23(15): 2129-2170.
7. Aich S, Pradhan P M, Park J, et al. A machine learning approach to distinguish Parkinson’s disease (PD) patient’s with shuffling gait from older adults based on gait signals using 3D motion analysis. Int J Eng Technol, 2018, 7(3.29): 153-156.
8. Slijepcevic D, Zeppelzauer M, Gorgas A M, et al. Automatic classification of functional gait disorders. IEEE J Biomed Health, 2017, 22(5): 1653-1661.
9. Reither L R, Foreman M H, Migotsky N, et al. Upper extremity movement reliability and validity of the Kinect version 2. Disabil Rehabil-Assi, 2018, 13(1): 54-59.
10. Zhu W, Anderson B, Zhu S, et al. A computer vision-based system for stride length estimation using a mobile phone camera// Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility. Reno: ACM, 2016: 121-130.
11. Nardo A, Anasetti F, Servello D, et al. Quantitative gait analysis in patients with Parkinson treated with deep brain stimulation: the effects of a robotic gait training. NeuroRehabilitation, 2014, 35(4): 779-788.
12. 刘俊杰. 基于机器学习与步态分析的帕金森病患者定量评估与分类方法研究. 杭州: 杭州电子科技大学, 2021.
13. Greene B R, McGrath D, O’Neill R, et al. An adaptive gyroscope-based algorithm for temporal gait analysis. Med Biol Eng Comput, 2010, 48(12): 1251-1260.
14. Lima A L S, Smits T, Darweesh S K L, et al. Home-based monitoring of falls using wearable sensors in Parkinson’s disease. Movement Disord, 2020, 35(1): 109-115.
15. 沈天毓, 王计平, 郭立泉, 等. 利用可穿戴设备对帕金森病患者运动功能进行量化评估. 生物医学工程学杂志, 2018, 35(2): 206-213.
16. 朱业安, 徐唯祎, 王睿, 等. 偏瘫步态障碍的自动识别和分析. 生物医学工程学杂志, 2019, 36(2): 306-314.
17. Kim A, Kim J, Rietdyk S, et al. A wearable smartphone-enabled camera-based system for gait assessment. Gait Posture, 2015, 42(2): 138-144.
18. Gabel M, Gilad-Bachrach R, Renshaw E, et al. Full body gait analysis with Kinect// 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego: IEEE, 2012: 1964-1967.
19. Verlekar T T, Soares L D, Correia P L. Automatic classification of gait impairments using a markerless 2D video-based system. Sensors, 2018, 18(9): 2743.
20. Stenum J, Rossi C, Roemmich R T. Two-dimensional video-based analysis of human gait using pose estimation. PLoS Comput Biol, 2021, 17(4): e1008935.
21. Kanko R M, Laende E K, Strutzenberger G, et al. Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system. J Biomech, 2021, 122: 110414.
22. Romijnders R, Warmerdam E, Hansen C, et al. Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson’s Disease patients. J Neuroeng Rehabil, 2021, 18(1): 28.
23. Jakob V, Küderle A, Kluge F, et al. Validation of a sensor-based gait analysis system with a gold-standard motion capture system in patients with Parkinson’s disease. Sensors, 2021, 21(22): 7680.
24. Veeraragavan S, Gopalai A A, Gouwanda D, et al. Parkinson’s disease diagnosis and severity assessment using ground reaction forces and neural networks. Front Physiol, 2020, 11: 587057.
25. Balaji E, Brindha D, Balakrishnan R. Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease. Appl Soft Comput, 2020, 94: 106494.
26. Bouten C V C, Koekkoek K T M, Verduin M, et al. A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE T Bio-Med Eng, 1997, 44(3): 136-147.
27. Stuberg W A, Colerick V L, Blanke D J, et al. Comparison of a clinical gait analysis method using videography and temporal-distance measures with 16-mm cinematography. Phys Ther, 1988, 68(8): 1221-1225.
28. Lewek M D, Poole R, Johnson J, et al. Arm swing magnitude and asymmetry during gait in the early stages of Parkinson’s disease. Gait Posture, 2010, 31(2): 256-260.

Journal of Biomedical Engineering

A two-dimensional video based quantification method and clinical application research of motion disorders

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