Research progress in motor assessment of neurodegenerative diseases driven by motion capture data_Journal of Biomedical Engineering

Authors：

WU Junlang ¹ , GUO Wei ² , LUO Kexin ¹ , HE Ling ¹ ,  YANG Guanci ¹

1. Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang 550025, P.R. China;
2. Guizhou Provincial Staff and Workers Hospital, Guiyang 550025, P. R. China;

Corresponding author：

Keywords：

Neurodegenerative diseases; Motion capture; Kinematic features; Motor assessment

DOI：

10.7507/1001-5515.202403004

Video：

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Neurodegenerative diseases (NDDs) are a group of heterogeneous neurological disorders that can cause progressive loss of neurons in the central nervous system or peripheral nervous system, resulting in a decline in motor function. Motion capture, as a high-precision and high-resolution technology for capturing human motion data, drives NDDs motor assessment to effectively extract kinematic features and thus assess the patient’s motor ability or disease severity. This paper focuses on the recent research progress in motor assessment of NDDs driven by motion capture data. Based on a brief introduction of NDDs motor assessment datasets, we categorized the assessment methods into three types according to the way of feature extraction and processing: NDDs motor assessment methods based on statistical analysis, machine learning and deep learning. Then, we comparatively analyzed the technical points and characteristics of the three types of methods from the aspects of data composition, data preprocessing, assessment methods, assessment purposes and effects. Finally, we discussed and prospected the development trends of NDDs motor assessment.

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2.	Benatar M, Wuu J, McHutchison C, et al. Preventing amyotrophic lateral sclerosis: insights from pre-symptomatic neurodegenerative diseases. Brain, 2022, 145(1): 27-44..
3.	Jia L, Du Y, Chu L, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health, 2020, 5(12): E661-E671..
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5.	Endo M, Nerrise F, Zhao Q, et al. Data-driven discovery of movement-linked heterogeneity in neurodegenerative diseases. Nat Mach Intell, 2024, 6(9): 1034-1045..
6.	Yozevitch R, Frenkel-Toledo S, Elion O, et al. Cost-effective and efficient solutions for the assessment and practice of upper extremity motor performance. IEEE Sens J, 2023, 23(19): 23494-23499..
7.	刘程林, 郝卫亚, 霍波. 运动生物力学发展现状及挑战. 力学进展, 2023, 53(1): 198-238..
8.	高经纬, 马超, 苏鸿, 等. 基于改进机器学习算法的步态识别与预测研究. 生物医学工程学杂志, 2022, 39(1): 103-111..
9.	Arizpe-Gomez P, Harms K, Janitzky K, et al. Towards automated self-administered motor status assessment: validation of a depth camera system for gait feature analysis. Biomed Signal Process Control, 2024, 87: 105352..
10.	Gholami M, Ward R, Mahal R, et al. Automatic labeling of Parkinson’s disease gait videos with weak supervision. Med Image Anal, 2023, 89: 102871..
11.	Park D J, Lee J W, Lee M J, et al. Evaluation for Parkinsonian bradykinesia by deep learning modeling of kinematic parameters. J Neural Transm, 2021, 128(2): 181-189..
12.	Vicedo C, Nieto-Reyes A, Bringas S, et al. Automatic apraxia detection using deep convolutional neural networks and similarity methods. Mach Vision Appl, 2023, 34(4): 60..
13.	Negin F, Rodriguez P, Koperski M, et al. PRAXIS: Towards automatic cognitive assessment using gesture recognition. Expert Syst Appl, 2018, 106: 21-35..
14.	Hayashida T, Sugiyama T, Sakai K, et al. Validation and discussion of severity evaluation and disease classification using tremor video. Electronics, 2023, 12(7): 1674..
15.	Zeng Q, Liu P, Yu N, et al. Video-based quantification of gait impairments in Parkinson’s disease using skeleton-silhouette fusion convolution network. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 2912-2922..
16.	Sabo A, Mehdizadeh S, Iaboni A, et al. Estimating Parkinsonism severity in natural gait videos of older adults with dementia. IEEE J Biomed Health Inf, 2022, 26(5): 2288-2298..
17.	Yu B X B, Liu Y, Chan K C C, et al. Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression. Pattern Recognit, 2021, 119: 108095..
18.	Bringas S, Salomon S, Duque R, et al. Alzheimer’s disease stage identification using deep learning models. J Biomed Inform, 2020, 109: 103514..
19.	Francisco Pedrero-Sanchez J, Belda-Lois J, Serra-Ano P, et al. Classification of healthy, Alzheimer and Parkinson populations with a multi-branch neural network. Biomed Signal Process Control, 2022, 75: 103617..
20.	Liu W, Lin X, Chen X, et al. Vision-based estimation of MDS-UPDRS scores for quantifying Parkinson’s disease tremor severity. Med Image Anal, 2023, 85: 102754..
21.	Morinan G, Dushin Y, Sarapata G, et al. Computer vision quantification of whole-body Parkinsonian bradykinesia using a large multi-site population. NPJ Parkinsons Dis, 2023, 9(1): 10..
22.	Guo R, Sun J, Zhang C, et al. A self-supervised metric learning framework for the arising-from-chair assessment of Parkinsonians with graph convolutional networks. IEEE Trans Circuits Syst Video Technol, 2022, 32(9): 6461-6471..
23.	Guo R, Shao X, Zhang C, et al. Multi-scale sparse graph convolutional network for the assessment of Parkinsonian gait. IEEE Trans Multimedia, 2022, 24: 1583-1594..
24.	Guo R, Sun J, Zhang C, et al. A contrastive graph convolutional network for toe-tapping assessment in Parkinson’s disease. IEEE Trans Circuits Syst Video Technol, 2022, 32(12): 8864-8874..
25.	Guo R, Li H, Zhang C, et al. A tree-structure-guided graph convolutional network with contrastive learning for the assessment of Parkinsonian hand movements. Med Image Anal, 2022, 81: 102560..
26.	Li H, Shao X, Zhang C, et al. Automated assessment of Parkinsonian finger-tapping tests through a vision-based fine-grained classification model. Neurocomputing, 2021, 441: 260-271..
27.	Amprimo G, Masi G, Priano L, et al. Assessment tasks and virtual exergames for remote monitoring of Parkinson’s disease: an integrated approach based on Azure Kinect. Sensors, 2022, 22(21): 8173..
28.	Luksys D, Jatuzis D, Jonaitis G, et al. Application of continuous relative phase analysis for differentiation of gait in neurodegenerative disease. Biomed Signal Process Control, 2021, 67: 102558..
29.	Iseki C, Suzuki S, Fukami T, et al. Fluctuations in upper and lower body movement during walking in normal pressure hydrocephalus and Parkinson’s disease assessed by motion capture with a smartphone application, TDPT-GT. Sensors, 2023, 23(22): 9263..
30.	Lassmann C, Ilg W, Schneider M, et al. Specific gait changes in prodromal hereditary spastic paraplegia type 4: preSPG4 study. Mov Disord, 2022, 37(12): 2417-2426..
31.	Peng K, Xie L, Hong R, et al. Early-onset and late-onset Parkinson’s disease exhibit a different profile of gait and posture features based on the Kinect. Neurol Sci, 2023, 45: 139-147..
32.	Vasylenko O, Gorecka M M, Waterloo K, et al. Reduction in manual asymmetry and decline in fine manual dexterity in right-handed older adults with mild cognitive impairment. Laterality, 2022, 27(6): 581-604..
33.	Zhao Y, Liu Y, Lu W, et al. Intelligent IoT anklets for monitoring the assessment of Parkinson’s diseases. IEEE Sens J, 2023, 23(24): 31523-31536..
34.	Yang N, Liu D, Liu T, et al. Automatic detection pipeline for accessing the motor severity of Parkinson’s disease in finger tapping and postural stability. IEEE Access, 2022, 10: 66961-66973..
35.	Iseki C, Hayasaka T, Yanagawa H, et al. Artificial intelligence distinguishes pathological gait: the analysis of markerless motion capture gait data acquired by an ios application (TDPT-GT). Sensors, 2023, 23(13): 6217..
36.	Ma L, Shi W, Chen C, et al. Remote scoring models of rigidity and postural stability of Parkinson’s disease based on indirect motions and a low-cost RGB algorithm. Front Aging Neurosci, 2023, 15: 1034376..
37.	Amprimo G, Rechichi I, Ferraris C, et al. Objective assessment of the finger tapping task in Parkinson’s disease and control subjects using Azure Kinect and machine learning// 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). L’Aquila: IEEE, 2023: 640-645..
38.	Hoang T, Zehni M, Xu H, et al. Towards a comprehensive solution for a vision-based digitized neurological examination. IEEE J Biomed Health, 2022, 26(8): 4020-4031..
39.	Guarin D L, Taati B, Abrahao A, et al. Video-based facial movement analysis in the assessment of bulbar amyotrophic lateral sclerosis: clinical validation. J Speech Lang Hear Res, 2022, 65(12): 4667-4678..
40.	孙玉波, 刘培培, 杨宇辰, 等. 一种基于二维视频的运动障碍量化评估方法与临床应用研究. 生物医学工程学杂志, 2023, 40(3): 499-507..
41.	Dai H, Cai G, Lin Z, et al. Validation of inertial sensing-based wearable device for tremor and bradykinesia quantification. IEEE J Biomed Health Inform, 2021, 25(4): 997-1005..
42.	Dadashzadeh A, Whone A, Rolinski M, et al. Exploring motion boundaries in an end-to-end network for vision-based Parkinson’s severity assessment// 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM). Setubal: SCITEPRESS, 2021: 89-97..
43.	Prakash P, Kaur R, Levy J, et al. A deep learning approach for grading of motor impairment severity in Parkinson’s disease// 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Sydney: IEEE, 2023: 1-4..
44.	Kaur R, Motl R W W, Sowers R, et al. A vision-based framework for predicting multiple sclerosis and Parkinson’s disease gait dysfunctions-a deep learning approach. IEEE J Biomed Health Inform, 2023, 27(1): 190-201..
45.	Lu M, Zhao Q, Poston K L, et al. Quantifying Parkinson’s disease motor severity under uncertainty using MDS-UPDRS videos. Med Image Anal, 2021, 73: 102179..
46.	Pedrero-Sánchez J F, Belda-Lois J M, Serra-Añó P, et al. Classification of Parkinson’s disease stages with a two-stage deep neural network. Front Aging Neurosci, 2023, 15: 1152917..
47.	Zeng Q, Liu P, Bai Y, et al. SlowFast GCN network for quantification of Parkinsonian gait using 2D videos// 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). Baishan: IEEE, 2022: 474-479..
48.	Zhang J, Lim J, Kim M, et al. WM-STGCN: a novel spatiotemporal modeling method for parkinsonian gait recognition. Sensors, 2023, 23(10): 4980..
49.	Chandrabhatla A S, Pomeraniec I J, Ksendzovsky A. Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson’s disease motor symptoms. NPJ Digit Med, 2022, 5(1): 32..
50.	Xu B, Yang G. Interpretability research of deep learning: a literature survey. Inf Fusion, 2025, 115: 102721..

1. Wilson D M, Cookson M R, van den Bosch L, et al. Hallmarks of neurodegenerative diseases. Cell, 2023, 186(4): 693-714..
2. Benatar M, Wuu J, McHutchison C, et al. Preventing amyotrophic lateral sclerosis: insights from pre-symptomatic neurodegenerative diseases. Brain, 2022, 145(1): 27-44..
3. Jia L, Du Y, Chu L, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health, 2020, 5(12): E661-E671..
4. Joza S, Hu M T, Jung K Y, et al. Progression of clinical markers in prodromal Parkinson’s disease and dementia with Lewy bodies: a multicentre study. Brain, 2023, 146(8): 3258-3272..
5. Endo M, Nerrise F, Zhao Q, et al. Data-driven discovery of movement-linked heterogeneity in neurodegenerative diseases. Nat Mach Intell, 2024, 6(9): 1034-1045..
6. Yozevitch R, Frenkel-Toledo S, Elion O, et al. Cost-effective and efficient solutions for the assessment and practice of upper extremity motor performance. IEEE Sens J, 2023, 23(19): 23494-23499..
7. 刘程林, 郝卫亚, 霍波. 运动生物力学发展现状及挑战. 力学进展, 2023, 53(1): 198-238..
8. 高经纬, 马超, 苏鸿, 等. 基于改进机器学习算法的步态识别与预测研究. 生物医学工程学杂志, 2022, 39(1): 103-111..
9. Arizpe-Gomez P, Harms K, Janitzky K, et al. Towards automated self-administered motor status assessment: validation of a depth camera system for gait feature analysis. Biomed Signal Process Control, 2024, 87: 105352..
10. Gholami M, Ward R, Mahal R, et al. Automatic labeling of Parkinson’s disease gait videos with weak supervision. Med Image Anal, 2023, 89: 102871..
11. Park D J, Lee J W, Lee M J, et al. Evaluation for Parkinsonian bradykinesia by deep learning modeling of kinematic parameters. J Neural Transm, 2021, 128(2): 181-189..
12. Vicedo C, Nieto-Reyes A, Bringas S, et al. Automatic apraxia detection using deep convolutional neural networks and similarity methods. Mach Vision Appl, 2023, 34(4): 60..
13. Negin F, Rodriguez P, Koperski M, et al. PRAXIS: Towards automatic cognitive assessment using gesture recognition. Expert Syst Appl, 2018, 106: 21-35..
14. Hayashida T, Sugiyama T, Sakai K, et al. Validation and discussion of severity evaluation and disease classification using tremor video. Electronics, 2023, 12(7): 1674..
15. Zeng Q, Liu P, Yu N, et al. Video-based quantification of gait impairments in Parkinson’s disease using skeleton-silhouette fusion convolution network. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 2912-2922..
16. Sabo A, Mehdizadeh S, Iaboni A, et al. Estimating Parkinsonism severity in natural gait videos of older adults with dementia. IEEE J Biomed Health Inf, 2022, 26(5): 2288-2298..
17. Yu B X B, Liu Y, Chan K C C, et al. Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression. Pattern Recognit, 2021, 119: 108095..
18. Bringas S, Salomon S, Duque R, et al. Alzheimer’s disease stage identification using deep learning models. J Biomed Inform, 2020, 109: 103514..
19. Francisco Pedrero-Sanchez J, Belda-Lois J, Serra-Ano P, et al. Classification of healthy, Alzheimer and Parkinson populations with a multi-branch neural network. Biomed Signal Process Control, 2022, 75: 103617..
20. Liu W, Lin X, Chen X, et al. Vision-based estimation of MDS-UPDRS scores for quantifying Parkinson’s disease tremor severity. Med Image Anal, 2023, 85: 102754..
21. Morinan G, Dushin Y, Sarapata G, et al. Computer vision quantification of whole-body Parkinsonian bradykinesia using a large multi-site population. NPJ Parkinsons Dis, 2023, 9(1): 10..
22. Guo R, Sun J, Zhang C, et al. A self-supervised metric learning framework for the arising-from-chair assessment of Parkinsonians with graph convolutional networks. IEEE Trans Circuits Syst Video Technol, 2022, 32(9): 6461-6471..
23. Guo R, Shao X, Zhang C, et al. Multi-scale sparse graph convolutional network for the assessment of Parkinsonian gait. IEEE Trans Multimedia, 2022, 24: 1583-1594..
24. Guo R, Sun J, Zhang C, et al. A contrastive graph convolutional network for toe-tapping assessment in Parkinson’s disease. IEEE Trans Circuits Syst Video Technol, 2022, 32(12): 8864-8874..
25. Guo R, Li H, Zhang C, et al. A tree-structure-guided graph convolutional network with contrastive learning for the assessment of Parkinsonian hand movements. Med Image Anal, 2022, 81: 102560..
26. Li H, Shao X, Zhang C, et al. Automated assessment of Parkinsonian finger-tapping tests through a vision-based fine-grained classification model. Neurocomputing, 2021, 441: 260-271..
27. Amprimo G, Masi G, Priano L, et al. Assessment tasks and virtual exergames for remote monitoring of Parkinson’s disease: an integrated approach based on Azure Kinect. Sensors, 2022, 22(21): 8173..
28. Luksys D, Jatuzis D, Jonaitis G, et al. Application of continuous relative phase analysis for differentiation of gait in neurodegenerative disease. Biomed Signal Process Control, 2021, 67: 102558..
29. Iseki C, Suzuki S, Fukami T, et al. Fluctuations in upper and lower body movement during walking in normal pressure hydrocephalus and Parkinson’s disease assessed by motion capture with a smartphone application, TDPT-GT. Sensors, 2023, 23(22): 9263..
30. Lassmann C, Ilg W, Schneider M, et al. Specific gait changes in prodromal hereditary spastic paraplegia type 4: preSPG4 study. Mov Disord, 2022, 37(12): 2417-2426..
31. Peng K, Xie L, Hong R, et al. Early-onset and late-onset Parkinson’s disease exhibit a different profile of gait and posture features based on the Kinect. Neurol Sci, 2023, 45: 139-147..
32. Vasylenko O, Gorecka M M, Waterloo K, et al. Reduction in manual asymmetry and decline in fine manual dexterity in right-handed older adults with mild cognitive impairment. Laterality, 2022, 27(6): 581-604..
33. Zhao Y, Liu Y, Lu W, et al. Intelligent IoT anklets for monitoring the assessment of Parkinson’s diseases. IEEE Sens J, 2023, 23(24): 31523-31536..
34. Yang N, Liu D, Liu T, et al. Automatic detection pipeline for accessing the motor severity of Parkinson’s disease in finger tapping and postural stability. IEEE Access, 2022, 10: 66961-66973..
35. Iseki C, Hayasaka T, Yanagawa H, et al. Artificial intelligence distinguishes pathological gait: the analysis of markerless motion capture gait data acquired by an ios application (TDPT-GT). Sensors, 2023, 23(13): 6217..
36. Ma L, Shi W, Chen C, et al. Remote scoring models of rigidity and postural stability of Parkinson’s disease based on indirect motions and a low-cost RGB algorithm. Front Aging Neurosci, 2023, 15: 1034376..
37. Amprimo G, Rechichi I, Ferraris C, et al. Objective assessment of the finger tapping task in Parkinson’s disease and control subjects using Azure Kinect and machine learning// 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). L’Aquila: IEEE, 2023: 640-645..
38. Hoang T, Zehni M, Xu H, et al. Towards a comprehensive solution for a vision-based digitized neurological examination. IEEE J Biomed Health, 2022, 26(8): 4020-4031..
39. Guarin D L, Taati B, Abrahao A, et al. Video-based facial movement analysis in the assessment of bulbar amyotrophic lateral sclerosis: clinical validation. J Speech Lang Hear Res, 2022, 65(12): 4667-4678..
40. 孙玉波, 刘培培, 杨宇辰, 等. 一种基于二维视频的运动障碍量化评估方法与临床应用研究. 生物医学工程学杂志, 2023, 40(3): 499-507..
41. Dai H, Cai G, Lin Z, et al. Validation of inertial sensing-based wearable device for tremor and bradykinesia quantification. IEEE J Biomed Health Inform, 2021, 25(4): 997-1005..
42. Dadashzadeh A, Whone A, Rolinski M, et al. Exploring motion boundaries in an end-to-end network for vision-based Parkinson’s severity assessment// 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM). Setubal: SCITEPRESS, 2021: 89-97..
43. Prakash P, Kaur R, Levy J, et al. A deep learning approach for grading of motor impairment severity in Parkinson’s disease// 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Sydney: IEEE, 2023: 1-4..
44. Kaur R, Motl R W W, Sowers R, et al. A vision-based framework for predicting multiple sclerosis and Parkinson’s disease gait dysfunctions-a deep learning approach. IEEE J Biomed Health Inform, 2023, 27(1): 190-201..
45. Lu M, Zhao Q, Poston K L, et al. Quantifying Parkinson’s disease motor severity under uncertainty using MDS-UPDRS videos. Med Image Anal, 2021, 73: 102179..
46. Pedrero-Sánchez J F, Belda-Lois J M, Serra-Añó P, et al. Classification of Parkinson’s disease stages with a two-stage deep neural network. Front Aging Neurosci, 2023, 15: 1152917..
47. Zeng Q, Liu P, Bai Y, et al. SlowFast GCN network for quantification of Parkinsonian gait using 2D videos// 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). Baishan: IEEE, 2022: 474-479..
48. Zhang J, Lim J, Kim M, et al. WM-STGCN: a novel spatiotemporal modeling method for parkinsonian gait recognition. Sensors, 2023, 23(10): 4980..
49. Chandrabhatla A S, Pomeraniec I J, Ksendzovsky A. Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson’s disease motor symptoms. NPJ Digit Med, 2022, 5(1): 32..
50. Xu B, Yang G. Interpretability research of deep learning: a literature survey. Inf Fusion, 2025, 115: 102721..

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Latest ArticlesResearch progress in motor assessment of neurodegenerative diseases driven by motion capture data

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