Volume 6, Issue 3 (Fall 2019)
jhbmi 2019, 6(3): 178-196 |
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Seifallahi M, Soltanizadeh H, Hassani Mehraban A, Khamseh F. Detection of Alzheimer’s Disease in Elder People Using Gait Analysis and Kinect Camera. jhbmi 2019; 6 (3) :178-196
URL: http://jhbmi.ir/article-1-374-en.html
URL: http://jhbmi.ir/article-1-374-en.html
Ph.D. in Electronic Engineering, Assistant Professor, Electronic Engineering Dept., Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
Abstract: (4542 Views)
Introduction: Gait analysis through using modern technology for detection of Alzheimer's disease has found special attention by researchers over the last decade. In this study, skeletal data recorded with a Kinect camera, were used to analyze gait for the purpose of detecting Alzheimer's disease in elders.
Method: In this applied-developmental experimental study, using a Kinect camera, data were collected for 12 elderly women with Alzheimer's disease and 12 healthy elderly women walking in an oval path. After extracting various features of gait, descriptive analysis was performed to compare the features between the healthy and patient groups. Then, a support vector machine classifier was designed to detect elderly people with Alzheimer's disease.
Results: The comparison of extracted features from skeletal data of gait using Kinect camera in this study indicate that the results are matched with previous findings from systems based on other types of sensors. The accuracy, sensitivity, precision and specificity of system designed in the present study for classifying elders with Alzheimer's disease and healthy elders were 91.25%, 93.4484%, 90.5945% and 93.581% respectively.
Conclusion: In addition to descriptive analysis of gait, by using machine learning methods such as support vector machine classifier, elderly people with Alzheimer's disease can be detected based on features extracted from skeletal data of Elderly people.
Method: In this applied-developmental experimental study, using a Kinect camera, data were collected for 12 elderly women with Alzheimer's disease and 12 healthy elderly women walking in an oval path. After extracting various features of gait, descriptive analysis was performed to compare the features between the healthy and patient groups. Then, a support vector machine classifier was designed to detect elderly people with Alzheimer's disease.
Results: The comparison of extracted features from skeletal data of gait using Kinect camera in this study indicate that the results are matched with previous findings from systems based on other types of sensors. The accuracy, sensitivity, precision and specificity of system designed in the present study for classifying elders with Alzheimer's disease and healthy elders were 91.25%, 93.4484%, 90.5945% and 93.581% respectively.
Conclusion: In addition to descriptive analysis of gait, by using machine learning methods such as support vector machine classifier, elderly people with Alzheimer's disease can be detected based on features extracted from skeletal data of Elderly people.
Keywords: Alzheimer Disease, Detection, Gait, Kinect Camera, Classification
eprint link: http://eprints.kmu.ac.ir/id/eprint/32275
eprint link: http://eprints.kmu.ac.ir/id/eprint/32275
Type of Study: Original Article |
Subject:
Clinical Decision Support Systems
Received: 2019/01/6 | Accepted: 2019/07/1
Received: 2019/01/6 | Accepted: 2019/07/1
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