Video-Based Gait Analysis for Assessing Alzheimer’s Disease and Dementia with Lewy Bodies - Archive ouverte HAL Access content directly
Journal Articles Lecture Notes in Computer Science Year : 2024

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

Abstract

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.
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Dates and versions

hal-04295939 , version 1 (20-11-2023)

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Diwei Wang, Chaima Zouaoui, Jinhyeok Jang, Hassen Drira, Hyewon Seo. Video-Based Gait Analysis for Assessing Alzheimer’s Disease and Dementia with Lewy Bodies. Lecture Notes in Computer Science, 2024, Lecture Notes in Computer Science, 14313, pp.72-82. ⟨10.1007/978-3-031-47076-9_8⟩. ⟨hal-04295939⟩
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