Early Alzheimer’s disease recognition via multimodal hand movement quality assessment_Journal of Biomedical Engineering

Authors：

 YANG Guanci ^1,3 , ZHU Chengcheng ¹ ,  WU Junlang ¹ , LUO Kexin ¹ , CHEN Xiaowen ² , LIN Jiacheng ^1,3

1. Key lab of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, P. R. China;
2. School of Biology and Engineering, Guizhou Medical University, Guiyang 550000, P. R. China;
3. Guizhou Province International Science & Technology Cooperation Base of Electronic Information and Intelligent Applications, Guizhou University, Guiyang 550025, P. R. China;

Corresponding author：

YANG Guanci, Email: gcyang@gzu.edu.cn; WU Junlang, Email: 2664707671@qq.com

Keywords：

Hand movements; Motion quality assessment; Feature fusion; Machine learning; Alzheimer’s disease recognition

DOI：

10.7507/1001-5515.202509029

Video：

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Abstract Full text Figures/Tables Video References Cited by

Alzheimer’s disease (AD) is a common elderly illness, and the hand movement abilities of patients differ from those of normal individuals. Focusing on the utilization of RGB, optical flow, and hand skeleton as tri-modal image information for early AD recognition, a method for early AD recognition via multi-modal hand motion quality assessment (EADR) is proposed. First, a hybrid modality feature encoder incorporating global contextual information was designed to integrate the global contextual information of features from three specific modality branches. Subsequently, a fusion modality feature decoder network incorporating specific modality features was proposed to decode the overlooked information in the fusion modality branch from specific modality features, thereby enhancing feature fusion. Experiments demonstrated that EADR effectively could capture high-quality hand motion features and excelled in hand motion quality assessment tasks, outperforming existing models. Based on this, the action quality scoring regression model trained using the k-nearest neighbors algorithm demonstrated the best recognition performance for AD patients, with Spearman’s rank correlation coefficient and Kendall’s rank correlation coefficient reaching 90.98% and 83.44%, respectively. This indicates that the assessment of hand motor ability may serve as a potential auxiliary tool for early AD identification.

Citation： YANG Guanci, ZHU Chengcheng, WU Junlang, LUO Kexin, CHEN Xiaowen, LIN Jiacheng. Early Alzheimer’s disease recognition via multimodal hand movement quality assessment. Journal of Biomedical Engineering, 2026, 43(1): 70-78. doi: 10.7507/1001-5515.202509029 Copy

1.	中国老年医学学会精神医学与心理健康分会. 早期阿尔茨海默病诊疗路径的精神科实践指导. 中华精神科杂志, 2024, 57(7): 407-413.
2.	Bloem B R, Okun M S, Klein C. Parkinson’s disease. Lancet, 2021, 397(10291): 2284-2303.
3.	Bischof G N, Jaeger E, Giehl K, et al. Cortical tau aggregation patterns associated with apraxia in patients with alzheimer disease. Neurology, 2024, 103(12): e210062.
4.	吴均浪, 郭威, 罗可欣, 等. 动作捕捉数据驱动的神经退行性疾病运动评估研究进展. 生物医学工程学杂志, 2025, 42(2): 396-403,408.
5.	Montero-Odasso M, Pieruccini-Faria F, Ismail Z, et al. CCCDTD5 recommendations on early non cognitive markers of dementia: a canadian consensus. Alzheimers Dement (N Y), 2020, 6(1): e12068.
6.	Na H K, Cho H, Lee H S, et al. Neural basis of motor symptoms in Alzheimer’s disease: role of regional tau burden and cognition. Alzheimers Dement (N Y), 2025, 21(8): e70598.
7.	崔建奇. 阿尔兹海默病. 西安: 陕西科学技术出版社, 2018.
8.	De Stefano C, Fontanella F, Impedovo D, et al. Handwriting analysis to support neurodegenerative diseases diagnosis: a review. Pattern Recognition Letters, 2019, 121: 37-45.
9.	Yu N, Chang S. Characterization of the fine motor problems in patients with cognitive dysfunction - a computerized handwriting analysis. Hum Mov Sci, 2019, 65: 71-79.
10.	Nardone E, D Alessandro T, De Stefano C, et al. A bayesian network combiner for multimodal handwriting analysis in Alzheimer’s disease detection. Pattern Recogn Lett, 2025, 190: 177-184.
11.	Müller S, Herde L, Preische O, et al. Diagnostic value of digital clock drawing test in comparison with cerad neuropsychological battery total score for discrimination of patients in the early course of Alzheimer’s disease from healthy individuals. Sci Rep, 2019, 9(1): 3543.
12.	Kim K W, Lee S Y, Choi J, et al. A comprehensive evaluation of the process of copying a complex figure in early- and late-onset Alzheimer disease: a quantitative analysis of digital pen data. J Med Internet Res, 2020, 22(8): e18136.
13.	Chan J Y C, Bat B K K, Wong A, et al. Evaluation of digital drawing tests and paper-and-pencil drawing tests for the screening of mild cognitive impairment and dementia: a systematic review and meta-analysis of diagnostic studies. Neuropsychol Rev, 2022, 32(3): 566-576.
14.	Roalf D R, Rupert P, Mechanic-Hamilton D, et al. Quantitative assessment of finger tapping characteristics in mild cognitive impairment, Alzheimer’s disease, and Parkinson’s disease. J Neurol, 2018, 265(6): 1365-1375.
15.	Colella D, Guerra A, Paparella G, et al. Motor dysfunction in mild cognitive impairment as tested by kinematic analysis and transcranial magnetic stimulation. Clin Neurophysiol, 2021, 132(2): 315-322.
16.	Wang X, St George R J, Bindoff A D, et al. Estimating presymptomatic episodic memory impairment using simple hand movement tests: a cross-sectional study of a large sample of older adults. Alzheimers Dement (N Y), 2024, 20(1): 173-182.
17.	Li R, Wang X, Lawler K, et al. Brief webcam test of hand movements predicts episodic memory, executive function, and working memory in a community sample of cognitively asymptomatic older adults. Alzheimers Dement (N Y), 2024, 16(1): e12520.
18.	Prigatano G P, Braga L W, Mcelvogue M, et al. Motor correlates of finger tapping variability in subjective memory complaints, mild cognitive impairment and probable Alzheimer’s disease. J Alzheimers Dis, 2025, 103(4): 1161-1170.
19.	Geladó S, Gómez-Ruiz I, Diéguez-Vide F. Gestures analysis during a picture description task: capacity to discriminate between healthy controls, mild cognitive impairment, and Alzheimer’s disease. J Neurolinguistics, 2022, 61: 101038.
20.	Li X, Shen M, Han Z, et al. The gesture imitation test in dementia with Lewy bodies and Alzheimer’s disease dementia. Front Neurol, 2022, 13: 950730.
21.	Alty J, Bai Q, Li R, et al. The tas test project: a prospective longitudinal validation of new online motor-cognitive tests to detect preclinical Alzheimer’s disease and estimate 5-year risks of cognitive decline and dementia. BMC Neurol, 2022, 22(1): 266.
22.	Yliranta A, Karjalainen V, Nuorva J, et al. Apraxia testing to distinguish early Alzheimer’s disease from psychiatric causes of cognitive impairment. Clin Neuropsychol, 2023, 37(8): 1629-1650.
23.	Papadopoulos G, Parissis D, Gotzamani-Psarrakou A, et al. Apraxia patterns for the differentiation between Alzheimer’s disease and frontotemporal dementia variants. Medicina, 2024, 60(3): 435.
24.	Takasaki A, Hashimoto M, Fukuhara R, et al. Gesture imitation performance in community-dwelling older people: assessment of a gesture imitation task in the screening and diagnosis of mild cognitive impairment and dementia. Psychogeriatrics, 2024, 24(2): 404-414.
25.	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need// Advances in Neural Information Processing Systems 30. Red Hook: Curran Associates, 2017: 5998-6008.
26.	Xia Y, Zhao Z. Cross-modal background suppression for audio-visual event localization// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 19989-19998.
27.	Wei D, Liu Y, Zhu X, et al. MSAF: Multimodal supervise-attention enhanced fusion for video anomaly detection. IEEE Signal Process Lett, 2022, 29: 2178-2182.
28.	Liu Y, Li S, Wu Y, et al. UMT: Unified multi-modal Transformers for joint video moment retrieval and highlight detection// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 3042-3051.
29.	Zeng L A, Zheng W S. Multimodal action quality assessment. IEEE Trans Image Process, 2024, 33: 1600-1613.
30.	Tang Y, Ni Z, Zhou J, et al. Uncertainty-aware score distribution learning for action quality assessment// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 9839-9848.
31.	Reed C J, Yue X, Nrusimha A, et al. Self-supervised pretraining improves self-supervised pretraining// Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa: IEEE, 2022: 2584-2594.
32.	Dadashzadeh A, Duan S, Whone A, et al. PECoP: Parameter efficient continual pretraining for action quality assessment// Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa: IEEE, 2024: 42-52.
33.	Yu X, Rao Y, Zhao W, et al. Group-aware contrastive regression for action quality assessment// Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 7919-7928.
34.	Xu A, Zeng L A, Zheng W S. Likert scoring with grade decoupling for long-term action assessment// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 3232-3241.
35.	Zhou K, Li J, Cai R, et al. CoFInAl: Enhancing action quality assessment with coarse-to-fine instruction alignment// Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. Jeju: IJCAI, 2024: 1771-1779.
36.	Liu J, Wang H, Zhou W, et al. Adaptive spatiotemporal graph transformer network for action quality assessment. IEEE Trans Circuits Syst Video Technol, 2025, 35(7): 6628-6639.
37.	Xu B, Yang G. Interpretability research of deep learning: A literature survey. Inf Fusion, 2025, 115: 102721.
38.	Yang F, Xu B, Lin J, et al. Early detection of Alzheimer’s disease based on leveraging multimodal features of the Clock Drawing Test. J Alzheimers Dis, 2026. DOI: 10.1177/13872877261423940.

1. 中国老年医学学会精神医学与心理健康分会. 早期阿尔茨海默病诊疗路径的精神科实践指导. 中华精神科杂志, 2024, 57(7): 407-413.
2. Bloem B R, Okun M S, Klein C. Parkinson’s disease. Lancet, 2021, 397(10291): 2284-2303.
3. Bischof G N, Jaeger E, Giehl K, et al. Cortical tau aggregation patterns associated with apraxia in patients with alzheimer disease. Neurology, 2024, 103(12): e210062.
4. 吴均浪, 郭威, 罗可欣, 等. 动作捕捉数据驱动的神经退行性疾病运动评估研究进展. 生物医学工程学杂志, 2025, 42(2): 396-403,408.
5. Montero-Odasso M, Pieruccini-Faria F, Ismail Z, et al. CCCDTD5 recommendations on early non cognitive markers of dementia: a canadian consensus. Alzheimers Dement (N Y), 2020, 6(1): e12068.
6. Na H K, Cho H, Lee H S, et al. Neural basis of motor symptoms in Alzheimer’s disease: role of regional tau burden and cognition. Alzheimers Dement (N Y), 2025, 21(8): e70598.
7. 崔建奇. 阿尔兹海默病. 西安: 陕西科学技术出版社, 2018.
8. De Stefano C, Fontanella F, Impedovo D, et al. Handwriting analysis to support neurodegenerative diseases diagnosis: a review. Pattern Recognition Letters, 2019, 121: 37-45.
9. Yu N, Chang S. Characterization of the fine motor problems in patients with cognitive dysfunction - a computerized handwriting analysis. Hum Mov Sci, 2019, 65: 71-79.
10. Nardone E, D Alessandro T, De Stefano C, et al. A bayesian network combiner for multimodal handwriting analysis in Alzheimer’s disease detection. Pattern Recogn Lett, 2025, 190: 177-184.
11. Müller S, Herde L, Preische O, et al. Diagnostic value of digital clock drawing test in comparison with cerad neuropsychological battery total score for discrimination of patients in the early course of Alzheimer’s disease from healthy individuals. Sci Rep, 2019, 9(1): 3543.
12. Kim K W, Lee S Y, Choi J, et al. A comprehensive evaluation of the process of copying a complex figure in early- and late-onset Alzheimer disease: a quantitative analysis of digital pen data. J Med Internet Res, 2020, 22(8): e18136.
13. Chan J Y C, Bat B K K, Wong A, et al. Evaluation of digital drawing tests and paper-and-pencil drawing tests for the screening of mild cognitive impairment and dementia: a systematic review and meta-analysis of diagnostic studies. Neuropsychol Rev, 2022, 32(3): 566-576.
14. Roalf D R, Rupert P, Mechanic-Hamilton D, et al. Quantitative assessment of finger tapping characteristics in mild cognitive impairment, Alzheimer’s disease, and Parkinson’s disease. J Neurol, 2018, 265(6): 1365-1375.
15. Colella D, Guerra A, Paparella G, et al. Motor dysfunction in mild cognitive impairment as tested by kinematic analysis and transcranial magnetic stimulation. Clin Neurophysiol, 2021, 132(2): 315-322.
16. Wang X, St George R J, Bindoff A D, et al. Estimating presymptomatic episodic memory impairment using simple hand movement tests: a cross-sectional study of a large sample of older adults. Alzheimers Dement (N Y), 2024, 20(1): 173-182.
17. Li R, Wang X, Lawler K, et al. Brief webcam test of hand movements predicts episodic memory, executive function, and working memory in a community sample of cognitively asymptomatic older adults. Alzheimers Dement (N Y), 2024, 16(1): e12520.
18. Prigatano G P, Braga L W, Mcelvogue M, et al. Motor correlates of finger tapping variability in subjective memory complaints, mild cognitive impairment and probable Alzheimer’s disease. J Alzheimers Dis, 2025, 103(4): 1161-1170.
19. Geladó S, Gómez-Ruiz I, Diéguez-Vide F. Gestures analysis during a picture description task: capacity to discriminate between healthy controls, mild cognitive impairment, and Alzheimer’s disease. J Neurolinguistics, 2022, 61: 101038.
20. Li X, Shen M, Han Z, et al. The gesture imitation test in dementia with Lewy bodies and Alzheimer’s disease dementia. Front Neurol, 2022, 13: 950730.
21. Alty J, Bai Q, Li R, et al. The tas test project: a prospective longitudinal validation of new online motor-cognitive tests to detect preclinical Alzheimer’s disease and estimate 5-year risks of cognitive decline and dementia. BMC Neurol, 2022, 22(1): 266.
22. Yliranta A, Karjalainen V, Nuorva J, et al. Apraxia testing to distinguish early Alzheimer’s disease from psychiatric causes of cognitive impairment. Clin Neuropsychol, 2023, 37(8): 1629-1650.
23. Papadopoulos G, Parissis D, Gotzamani-Psarrakou A, et al. Apraxia patterns for the differentiation between Alzheimer’s disease and frontotemporal dementia variants. Medicina, 2024, 60(3): 435.
24. Takasaki A, Hashimoto M, Fukuhara R, et al. Gesture imitation performance in community-dwelling older people: assessment of a gesture imitation task in the screening and diagnosis of mild cognitive impairment and dementia. Psychogeriatrics, 2024, 24(2): 404-414.
25. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need// Advances in Neural Information Processing Systems 30. Red Hook: Curran Associates, 2017: 5998-6008.
26. Xia Y, Zhao Z. Cross-modal background suppression for audio-visual event localization// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 19989-19998.
27. Wei D, Liu Y, Zhu X, et al. MSAF: Multimodal supervise-attention enhanced fusion for video anomaly detection. IEEE Signal Process Lett, 2022, 29: 2178-2182.
28. Liu Y, Li S, Wu Y, et al. UMT: Unified multi-modal Transformers for joint video moment retrieval and highlight detection// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 3042-3051.
29. Zeng L A, Zheng W S. Multimodal action quality assessment. IEEE Trans Image Process, 2024, 33: 1600-1613.
30. Tang Y, Ni Z, Zhou J, et al. Uncertainty-aware score distribution learning for action quality assessment// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 9839-9848.
31. Reed C J, Yue X, Nrusimha A, et al. Self-supervised pretraining improves self-supervised pretraining// Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa: IEEE, 2022: 2584-2594.
32. Dadashzadeh A, Duan S, Whone A, et al. PECoP: Parameter efficient continual pretraining for action quality assessment// Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa: IEEE, 2024: 42-52.
33. Yu X, Rao Y, Zhao W, et al. Group-aware contrastive regression for action quality assessment// Proceedings of the IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 7919-7928.
34. Xu A, Zeng L A, Zheng W S. Likert scoring with grade decoupling for long-term action assessment// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 3232-3241.
35. Zhou K, Li J, Cai R, et al. CoFInAl: Enhancing action quality assessment with coarse-to-fine instruction alignment// Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. Jeju: IJCAI, 2024: 1771-1779.
36. Liu J, Wang H, Zhou W, et al. Adaptive spatiotemporal graph transformer network for action quality assessment. IEEE Trans Circuits Syst Video Technol, 2025, 35(7): 6628-6639.
37. Xu B, Yang G. Interpretability research of deep learning: A literature survey. Inf Fusion, 2025, 115: 102721.
38. Yang F, Xu B, Lin J, et al. Early detection of Alzheimer’s disease based on leveraging multimodal features of the Clock Drawing Test. J Alzheimers Dis, 2026. DOI: 10.1177/13872877261423940.

Journal of Biomedical Engineering

Early Alzheimer’s disease recognition via multimodal hand movement quality assessment

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