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:: Volume 22, Issue 1 (زمستان 1398) ::
Koomesh 1398, 22(1): 10-27 Back to browse issues page
A survey on Alzheimer’s disease detection using gait analysis
Mahmoud Seyfollahi , Hadi Soltanizadeh , Afsoon Hasani Mehraban , Fatemeh Khamseh
Abstract:   (6330 Views)
Introduction: Early detection of Alzheimer's disease (AD), as a neurodegenerative disease and the most common cause of dementia in the elderly people, using gait analysis have been particularly noted by researcher in recent years. Because this novel method is non-invasive, less cost, and feasible in non-clinical and laboratory environments. Therefore, in this Meta-Analysis review article, detection of AD using gait analysis based on information technology tools surveyed in previous studies. Materials and Methods: The search for previous articles was done in databases such as Google Scholar, Pubmed, IEEE, Springer, and Elsevier. After finding articles in these databases, appropriate articles were selected to survey based on criteria such as focusing of the study on elderly patients with AD or mild cognitive impairment, the detection and assessment of AD, and the use of sensor technology to record gait. Results: AD can be detected, even in the early stages and also at a stage of mild cognitive impairment based on gait analysis using new technologies with sensors and information technologies. Also, it is possible to quantitatively evaluate and more accurately detect the disease based on the combination of different sensor technologies and the use of artificial intelligence techniques. Conclusion: Although gait analysis can be a novel tool for early detection of AD, but it is essential to be developed and improved based on sensors and artificial intelligence in order to be used as a reliable clinical tools.
Keywords: Alzheimer Disease, Gait, Information Technology, Computer‐Assisted Image Processing, Machine Learning, Artificial Intelligence
Full-Text [PDF 1409 kb]   (2590 Downloads)    
Type of Study: Review | Subject: General
Received: 2018/09/25 | Accepted: 2019/06/25 | Published: 2020/01/4
References
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26. [26] Rucco R, Agosti V, Jacini F, Sorrentino P, Varriale P, Stefano MD. Spatio-temporal and kinematic gait analysis in patients with Frontotemporal dementia and Alzheimer's disease through 3D motion capture. Gait Posture 2016; 52: 312-317. [DOI:10.1016/j.gaitpost.2016.12.021]
27. [27] Borges SD, Radanovic M, Forlenza OV. Correlation between functional mobility and cognitive performance in older adults with cognitive impairment. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 2016; 25: 23-32. [DOI:10.1080/13825585.2016.1258035]
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35. [35] Ardlea RM, Morris R, Hickey A, Dina SD, Koychev I, Gunnc RN. Gait in mild Alzheimer's disease: feasibility of multi-center measurement in the clinic and home with body-worn sensors: a pilot study. J Alzheimers Dis 2018; 63: 331-341. [DOI:10.3233/JAD-171116]
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39. [39] Pourghayoomi E, Negahdar F, Shahidi G, Mehraban AH, Ebrahimi I, Taghizadeh G, et al. Correlation between functional balance and mobility tests and postural sway measures in dual task paradigm in Parkinson's disease (a pilot study). J Basic Clin Pathophysiol 2014; 2: 1-12.
40. [40] Allan LM, Ballard C G, Burn D J, Kenny RA. Prevalence and severity of gait disorders in Alzheimer's and non-Alzheimer's dementias. J Am Geriatr Soc 2005; 53: 1681-1687. [DOI:10.1111/j.1532-5415.2005.53552.x]
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50. [3] Ravari A, Mirzaei T, Salamizadeh A, Askari Majdabadi H. Effect of the spiritual care training on anxiety reduction in home caregivers of the elderly with Alzheimer disease. Koomesh 2017; 19: 467-474. (Persian).
51. [4] Aramendi AA, Aztiria A, Basarab A. On the early diagnosis of Alzheimer's disease from multimodal signals: A survey. Artif Intell Med 2016; 71: 1-29. [DOI:10.1016/j.artmed.2016.06.003]
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53. [6] Malouin F, Richards CL, Jackson PL, Dumas F, Doyon J. Brain activations during motor imagery of locomotor-related tasks: a PET study. Hum Brain Mapp 2003; 19: 47-62. [DOI:10.1002/hbm.10103]
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55. [8] Lam KY, Tsang WHN, Han S, Zhang W, Ng JKY, Nath A. Activity tracking and monitoring of patients with alzheimer's disease. Multimed Tools Appl 2017; 76: 489-521. [DOI:10.1007/s11042-015-3047-x]
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58. [11] Beauchet O, Allali G, Berrut G, Hommet C, Dubost V, Assal F. Gait analysis in demented subjects: Interests and perspectives. Neuropsychiatr Dis Treat 2008; 4: 155-160. [DOI:10.2147/NDT.S2070]
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61. [14] Maquet D, Lekeu F, Warzee E, Gillain S, Wojtasik V, Salmon E, et al. Gait analysis in elderly adult patients with mild cognitive impairment and patients with mild Alzheimer's disease: simple versus dual task: a preliminary report. Clin Physiol Funct Imaging 2010; 30: 51-56. [DOI:10.1111/j.1475-097X.2009.00903.x]
62. [15] Choi JS, Oh HS, Kang DW, Mun KR, Choi MH, Lee SJ, et al. Comparison of gait and cognitive function among the elderly with Alzheimer's disease, mild cognitive impairment and healthy. Int J Prec Engin Manufact 2011; 12: 169-173. [DOI:10.1007/s12541-011-0024-9]
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77. [30] MacAulay RK, Wagner MT, Szeles D, Milano NJ. Improving sensitivity to detect mild cognitive impairment: cognitive load dual-task gait speed assessment. J Int Neuropsychol Soc 2017; 23: 493-501. [DOI:10.1017/S1355617717000261]
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79. [32] Varatharajan R, Manogaran G, Priyan MK. Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Comput 2017; 1-10. [DOI:10.1007/s10586-017-0977-2]
80. [33] Callisaya ML, PLaunay CP, Srikanth VK, Verghese J, Allali G, Beauchet O. Cognitive status, fast walking speed and walking speed reserve-the Gait and Alzheimer interactions tracking (GAIT) study. Gero Sci 2017; 39: 231-239. [DOI:10.1007/s11357-017-9973-y]
81. [34] Hausdorff JM, Hillel I, Shustak S, Din SD, Bekkers EM, Pelosin E. Everyday stepping quantity and quality among older adult fallers with and without mild cognitive impairment: initial evidence for new motor markers of cognitive deficits? J Gerontol A Biol Sci Med Sci 2017; 73: 1-5. [DOI:10.1093/gerona/glx187]
82. [35] Ardlea RM, Morris R, Hickey A, Dina SD, Koychev I, Gunnc RN. Gait in mild Alzheimer's disease: feasibility of multi-center measurement in the clinic and home with body-worn sensors: a pilot study. J Alzheimers Dis 2018; 63: 331-341. [DOI:10.3233/JAD-171116]
83. [36] Lee L, Grimson WE. Gait analysis for recognition and classification. Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition; 2002 May 21-21; Washington, DC, USA, p.1-8.
84. [37] Valkanova V, Ebmeier K P. What can gait tell us about dementia? Review of epidemiological and neuropsychological evidence. Gait Posture 2017; 53: 215-223. [DOI:10.1016/j.gaitpost.2017.01.024]
85. [38] Pirker W, Katzenschlager R. Gait disorders in adults and the elderly: A clinical guide. Wien Klin Wochenschr 2017; 129: 81-95. [DOI:10.1007/s00508-016-1096-4]
86. [39] Pourghayoomi E, Negahdar F, Shahidi G, Mehraban AH, Ebrahimi I, Taghizadeh G, et al. Correlation between functional balance and mobility tests and postural sway measures in dual task paradigm in Parkinson's disease (a pilot study). J Basic Clin Pathophysiol 2014; 2: 1-12.
87. [40] Allan LM, Ballard C G, Burn D J, Kenny RA. Prevalence and severity of gait disorders in Alzheimer's and non-Alzheimer's dementias. J Am Geriatr Soc 2005; 53: 1681-1687. [DOI:10.1111/j.1532-5415.2005.53552.x]
88. [41] Herran AM, Garcia-Zapirain B, Mendez-Zorrilla A. Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 2014; 14: 3362-3395. [DOI:10.3390/s140203362]
89. [42] Tao W, Liu T, Zheng R, Feng H. Gait analysis using wearable sensors. Sensors 2012; 12: 2255-2283. [DOI:10.3390/s120202255]
90. [43] Schneider B, Banerjee T. Activity recognition using imagery for smart home monitoring. Adv Soft Comput Machine Learn Image Proc Studi Comput Int 2017; 730: 355-371. [DOI:10.1007/978-3-319-63754-9_16]
91. [44] Do TN, Suh YS. Gait analysis using floor markers and inertial sensors. Sensors 2012; 12: 1594-1611. [DOI:10.3390/s120201594]
92. [45] Middleton, L, Buss AA, Bazin A, Nixon MS. A floor sensor system for gait recognition. Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies; 2005 Oct 17-18; Buffalo, NY, USA, p.171-176.
93. [46] Chen SW, Lin SH, Liao LD, Lai HY, Pei YC, Kuo TS, et al. Quantification and recognition of parkinsonian gait from monocular video imaging using kernel based principal component analysis. Biomed Eng Online 2011; 10: 1-21. [DOI:10.1186/1475-925X-10-99]
94. [47] Imani Z, Soltanizadeh H. Person reidentification using local pattern descriptors and anthropometric measures from videos of Kinect sensor. IEEE Sensors J 2016; 16: 6227-6238. [DOI:10.1109/JSEN.2016.2579645]
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Seyfollahi M, Soltanizadeh H, Hasani Mehraban A, Khamseh F. A survey on Alzheimer’s disease detection using gait analysis. Koomesh 1398; 22 (1) :10-27
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Volume 22, Issue 1 (زمستان 1398) Back to browse issues page
کومش Koomesh
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