Alzheimer’s disease detection using skeleton data recorded with Kinect camera


Alzheimer’s disease (AD) is a neurodegenerative disease that leads to defects in cognitive and functional abilities of elderly people. In this paper, a novel methodology is presented to detect Alzheimer’s disease using recorded skeleton data with a KinectV.2 camera from the subject’s gait. After clinical assessment, the single-task walking test done by subjects was recorded with the kinectV.2 camera. Then, some descriptive statistical analyses were performed on the extracted features of recorded gait to compare them between people with Alzheimer’s disease and people without any cognitive impairment as the healthy control (HC) group. Then, a support vector machine classifier with different kernels was designed to classify subjects to AD and HC groups. The results show that the proposed method has acceptable results in comparison to previous studies to detect AD. The proposed method in this article has the accuracy, sensitivity, precision, and specificity of 92.31%, 96.33%, 88.62%, and 90.81% respectively to classify subjects to AD and HC groups.

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The authors would like to thank the Iran Alzheimer’s Association (IAA) for its contribution to this study. In particular, we would like to thank all the subjects in this research and their families who agree to participate in this study.

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Correspondence to Hadi Soltanizadeh.

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Seifallahi, M., Soltanizadeh, H., Hassani Mehraban, A. et al. Alzheimer’s disease detection using skeleton data recorded with Kinect camera. Cluster Comput 23, 1469–1481 (2020).

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  • Alzheimer detection
  • Gait analysis
  • Kinect camera
  • Classification
  • Support vector machine