A study of cognitive impairment quantitative assessment method based on gait characteristics_Journal of Biomedical Engineering

Authors：

 TAO Shuai ¹ , HU Hongbin ¹ , KONG Liwen ¹ , LYU Zeping ² , WANG Zumin ³ , ZHAO Jie ⁴ , LIU Shuang ⁵

1. Dalian Key Laboratory of Smart Medical and Health, Dalian University, Dalian, Liaoning 116622, P. R. China;
2. Internal Medicine-Neurology, Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center, Beijing 100176, P. R. China;
3. Graduate School, Dalian University, Dalian, Liaoning 116622, P. R. China;
4. Internal Medicine-Neurology, Zhongshan Hospital Affiliated to Dalian University, Dalian, Liaoning 116001, P. R. China;
5. Institute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300192, P. R. China;

Corresponding author：

TAO Shuai, Email: taoshuai@dlu.edu.cn

Keywords：

Cognitive dysfunction; Mild cognitive impairment; Gait analysis; Machine learning; Random forest

DOI：

10.7507/1001-5515.202305019

Video：

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Alzheimer’s disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject’s MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD in the future.

Citation： TAO Shuai, HU Hongbin, KONG Liwen, LYU Zeping, WANG Zumin, ZHAO Jie, LIU Shuang. A study of cognitive impairment quantitative assessment method based on gait characteristics. Journal of Biomedical Engineering, 2024, 41(2): 281-287. doi: 10.7507/1001-5515.202305019 Copy

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2.	Gould N, Kendall T. Developing the NICE/SCIE guidelines for dementia care: The challenges of enhancing the evidence base for social and health care. Br J Soc Work, 2007, 37(3): 475-490.
3.	Folstein M F, Folstein S E, Mchugh P R. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res, 1975, 12(3): 189-198.
4.	Nasreddine Z S, Phillips N A, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc, 2005, 53(4): 695-699.
5.	陶帅, 韩星, 孔丽文, 等. 基于步态的机器学习模型识别遗忘型轻度认知障碍和阿尔茨海默病. 中国全科医学, 2022, 25(31): 3857-3865.
6.	Wu S, MatsuurA T, Okura F, et al. Detecting lower MMSE scores in older adults using cross-trial features from a dual-task with gait and arithmetic. IEEE Access, 2021, 9: 150268-150282.
7.	Jung D, Kim J, Kim M, et al. Classifying the risk of cognitive impairment using sequential gait characteristics and long short-term memory networks. IEEE J Biomed Health Inform, 2021, 25(10): 4029-4040.
8.	Saho K, Uemura K, Sugano K, et al. Using micro-Doppler radar to measure gait features associated with cognitive functions in elderly adults. IEEE Access, 2019, 7: 24122-24131.
9.	Noh B, Yoon H, Youm C, et al. Prediction of decline in global cognitive function using machine learning with feature ranking of gait and physical fitness outcomes in older adults. Int J Environ Res Public Health, 2021, 18(21): 11347.
10.	Matsuura T, Sakashita K, Grushnikov A, et al. Statistical analysis of dual-task gait characteristics for cognitive score estimation. Sci Rep, 2019, 9(1): 1-12.
11.	Jensen A R, Rohwer Jr W D. The Stroop color-word test: a review. Acta Psychol, 1966, 25: 36-93.
12.	Bucks R S, Ashworth D L, Wilcock G K, et al. Assessment of activities of daily living in dementia: development of the Bristol Activities of Daily Living Scale. Age Ageing, 1996, 25(2): 113-120.
13.	Tao S, Zhang X, Cai H, et al. Gait based biometric personal authentication by using MEMS inertial sensors. J Ambient Intell Humanized Comput, 2018, 9(5): 1705-1712.
14.	陶帅, 吕泽平, 谢海群. 可穿戴步态辅助技术在康复养老领域中的应用. 科技导报, 2019, 37(22): 19-25.
15.	White R, Agouris I, Fletcher E. Harmonic analysis of force platform data in normal and cerebral palsy gait. Clin Biomech, 2005, 20(5): 508-516.
16.	Muir-Hunter S W, Montero-Odasso M. Gait cost of using a mobility aid in older adults with Alzheimer’s disease. J Am Geriatr Soc, 2016, 64(2): 437-438.
17.	Pullanagari R R, Kereszturi G, Yule I. Integrating airborne hyperspectral, topographic, and soil data for estimating pasture quality using recursive feature elimination with random forest regression. Remote Sens, 2018, 10(7): 1117.
18.	Utkin L V. An imprecise extension of SVM-based machine learning models. Neurocomputing, 2019, 331: 18-32.
19.	Phan D, Nguyen N, Pathirana P N, et al. A random forest approach for quantifying gait ataxia with truncal and peripheral measurements using multiple wearable sensors. IEEE Sensors J, 2019, 20(2): 723-734.
20.	Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B, 2005, 67(2): 301-320.
21.	Cao Ying, Miao Qiguang, Liu Jiachen, et al. Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica, 2013, 39(6): 745-758.
22.	Rao H, Shi X, Rodrigue A K, et al. Feature selection based on artificial bee colony and gradient boosting decision tree. Appl Soft Comput, 2019, 74: 634-642.
23.	Yao M, Zhu Y, Li J, et al. Research on predicting line loss rate in low voltage distribution network based on gradient boosting decision tree. Energies, 2019, 12(13): 2522.
24.	Lindh-Rengifo M, Jonasson S B, Ullén S, et al. Components of gait in people with and without mild cognitive impairment. Gait Posture, 2022, 93: 83-89.
25.	Schlachetzki J, Barth J, Marxreiter F, et al. Wearable sensors objectively measure gait parameters in Parkinson’s disease. PLoS One, 2017, 12(10): e0183989.
26.	Ni L, Lv W, Sun D, et al. Pathological gait signatures of post stroke dementia with toe-off and heel-to-ground angles discriminate from Alzheimer’s disease. Front Aging Neurosci, 2021, 13: 766884.
27.	Pieruccini-Faria F, Sarquis-adamson Y, Montero-odasso M. Mild cognitive impairment affects obstacle negotiation in older adults: results from “Gait and Brain Study”. Gerontology, 2019, 65(2): 164-173.
28.	Ricciardi C, Amboni M, De Santis C, et al. Machine learning can detect the presence of mild cognitive impairment in patients affected by Parkinson’s Disease// 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA). BariI: EEE, 2020: 1-6.
29.	Ginis P, Pirani R, Basaia S, et al. Focusing on heel strike improves toe clearance in people with Parkinson’s disease: an observational pilot study. Physiotherapy, 2017, 103(4): 485-490.
30.	Alcock L, Galna B, Lord S, et al. Characterisation of foot clearance during gait in people with early Parkinson’s disease: Deficits associated with a dual task. J Biomech, 2016, 49(13): 2763-2769.

1. Livingston G, Sommerlad A, Orgeta V, et al. Dementia prevention, intervention, and care. Lancet, 2017, 390(10113): 2673-2734.
2. Gould N, Kendall T. Developing the NICE/SCIE guidelines for dementia care: The challenges of enhancing the evidence base for social and health care. Br J Soc Work, 2007, 37(3): 475-490.
3. Folstein M F, Folstein S E, Mchugh P R. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res, 1975, 12(3): 189-198.
4. Nasreddine Z S, Phillips N A, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc, 2005, 53(4): 695-699.
5. 陶帅, 韩星, 孔丽文, 等. 基于步态的机器学习模型识别遗忘型轻度认知障碍和阿尔茨海默病. 中国全科医学, 2022, 25(31): 3857-3865.
6. Wu S, MatsuurA T, Okura F, et al. Detecting lower MMSE scores in older adults using cross-trial features from a dual-task with gait and arithmetic. IEEE Access, 2021, 9: 150268-150282.
7. Jung D, Kim J, Kim M, et al. Classifying the risk of cognitive impairment using sequential gait characteristics and long short-term memory networks. IEEE J Biomed Health Inform, 2021, 25(10): 4029-4040.
8. Saho K, Uemura K, Sugano K, et al. Using micro-Doppler radar to measure gait features associated with cognitive functions in elderly adults. IEEE Access, 2019, 7: 24122-24131.
9. Noh B, Yoon H, Youm C, et al. Prediction of decline in global cognitive function using machine learning with feature ranking of gait and physical fitness outcomes in older adults. Int J Environ Res Public Health, 2021, 18(21): 11347.
10. Matsuura T, Sakashita K, Grushnikov A, et al. Statistical analysis of dual-task gait characteristics for cognitive score estimation. Sci Rep, 2019, 9(1): 1-12.
11. Jensen A R, Rohwer Jr W D. The Stroop color-word test: a review. Acta Psychol, 1966, 25: 36-93.
12. Bucks R S, Ashworth D L, Wilcock G K, et al. Assessment of activities of daily living in dementia: development of the Bristol Activities of Daily Living Scale. Age Ageing, 1996, 25(2): 113-120.
13. Tao S, Zhang X, Cai H, et al. Gait based biometric personal authentication by using MEMS inertial sensors. J Ambient Intell Humanized Comput, 2018, 9(5): 1705-1712.
14. 陶帅, 吕泽平, 谢海群. 可穿戴步态辅助技术在康复养老领域中的应用. 科技导报, 2019, 37(22): 19-25.
15. White R, Agouris I, Fletcher E. Harmonic analysis of force platform data in normal and cerebral palsy gait. Clin Biomech, 2005, 20(5): 508-516.
16. Muir-Hunter S W, Montero-Odasso M. Gait cost of using a mobility aid in older adults with Alzheimer’s disease. J Am Geriatr Soc, 2016, 64(2): 437-438.
17. Pullanagari R R, Kereszturi G, Yule I. Integrating airborne hyperspectral, topographic, and soil data for estimating pasture quality using recursive feature elimination with random forest regression. Remote Sens, 2018, 10(7): 1117.
18. Utkin L V. An imprecise extension of SVM-based machine learning models. Neurocomputing, 2019, 331: 18-32.
19. Phan D, Nguyen N, Pathirana P N, et al. A random forest approach for quantifying gait ataxia with truncal and peripheral measurements using multiple wearable sensors. IEEE Sensors J, 2019, 20(2): 723-734.
20. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B, 2005, 67(2): 301-320.
21. Cao Ying, Miao Qiguang, Liu Jiachen, et al. Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica, 2013, 39(6): 745-758.
22. Rao H, Shi X, Rodrigue A K, et al. Feature selection based on artificial bee colony and gradient boosting decision tree. Appl Soft Comput, 2019, 74: 634-642.
23. Yao M, Zhu Y, Li J, et al. Research on predicting line loss rate in low voltage distribution network based on gradient boosting decision tree. Energies, 2019, 12(13): 2522.
24. Lindh-Rengifo M, Jonasson S B, Ullén S, et al. Components of gait in people with and without mild cognitive impairment. Gait Posture, 2022, 93: 83-89.
25. Schlachetzki J, Barth J, Marxreiter F, et al. Wearable sensors objectively measure gait parameters in Parkinson’s disease. PLoS One, 2017, 12(10): e0183989.
26. Ni L, Lv W, Sun D, et al. Pathological gait signatures of post stroke dementia with toe-off and heel-to-ground angles discriminate from Alzheimer’s disease. Front Aging Neurosci, 2021, 13: 766884.
27. Pieruccini-Faria F, Sarquis-adamson Y, Montero-odasso M. Mild cognitive impairment affects obstacle negotiation in older adults: results from “Gait and Brain Study”. Gerontology, 2019, 65(2): 164-173.
28. Ricciardi C, Amboni M, De Santis C, et al. Machine learning can detect the presence of mild cognitive impairment in patients affected by Parkinson’s Disease// 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA). BariI: EEE, 2020: 1-6.
29. Ginis P, Pirani R, Basaia S, et al. Focusing on heel strike improves toe clearance in people with Parkinson’s disease: an observational pilot study. Physiotherapy, 2017, 103(4): 485-490.
30. Alcock L, Galna B, Lord S, et al. Characterisation of foot clearance during gait in people with early Parkinson’s disease: Deficits associated with a dual task. J Biomech, 2016, 49(13): 2763-2769.

Journal of Biomedical Engineering

A study of cognitive impairment quantitative assessment method based on gait characteristics

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