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Video-Based Gait Analysis for Assessing Alzheimer’s Disease and Dementia with Lewy Bodies

Lecture notes in computer science, 2024, Vol.14313, p.72-82 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0302-9743 ;EISSN: 1611-3349 ;DOI: 10.1007/978-3-031-47076-9_8

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  • Title:
    Video-Based Gait Analysis for Assessing Alzheimer’s Disease and Dementia with Lewy Bodies
  • Author: Wang, Diwei ; Zouaoui, Chaima ; Jang, Jinhyeok ; Drira, Hassen ; Seo, Hyewon
  • Subjects: Computer Science
  • Is Part Of: Lecture notes in computer science, 2024, Vol.14313, p.72-82
  • Description: Dementia with Lewy Bodies (DLB) and Alzheimer's Disease (AD) are two common neurodegenerative diseases among elderly people. Gait analysis plays a significant role in clinical assessments to discriminate these neurological disorders from healthy controls, to grade disease severity, and to further differentiate dementia subtypes. In this paper, we propose a deep-learning based model specifically designed to evaluate gait impairment score for assessing the dementia severity using monocular gait videos. Named MAX-GR, our model estimates the sequence of 3D body skeletons, applies corrections based on spatio-temporal gait features extracted from the input video, and performs classification on the corrected 3D pose sequence to determine the MDS-UPDRS gait scores. Experimental results show that our technique outperforms alternative state-of-the-art methods. The code, demo videos, as well as 3D skeleton dataset is available at https://github.com/lisqzqng/Video-based-g ait-analysis-for-dementia.
  • Publisher: Springer
  • Language: English
  • Identifier: ISSN: 0302-9743
    EISSN: 1611-3349
    DOI: 10.1007/978-3-031-47076-9_8
  • Source: Hyper Article en Ligne (HAL) (Open Access)
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