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

Diwei Wang (1, 2) , Chaima Zouaoui (3) , Jinhyeok Jang (4) , Hassen Drira (1, 2) , Hyewon Seo (1, 5)

1 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
2 UNISTRA - Université de Strasbourg
3 Ecole Polytechnique de Tunisie
4 ETRI - Electronics and Telecommunications Research Institute [DaeJeon]
5 CNRS - Centre National de la Recherche Scientifique

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.

Keywords: Gait impairment score; Dementia subtypes; Human 3D motion estimation; Geometric deep learning

Domains: Computer Science [cs]

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⟩