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.