  Recent years have witnessed an increasing global population affected by
neurodegenerative diseases (NDs), which traditionally require extensive
healthcare resources and human effort for medical diagnosis and monitoring. As
a crucial disease-related motor symptom, human gait can be exploited to
characterize different NDs. The current advances in artificial intelligence
(AI) models enable automatic gait analysis for NDs identification and
classification, opening a new avenue to facilitate faster and more
cost-effective diagnosis of NDs. In this paper, we provide a comprehensive
survey on recent progress of machine learning and deep learning based AI
techniques applied to diagnosis of five typical NDs through gait. We provide an
overview of the process of AI-assisted NDs diagnosis, and present a systematic
taxonomy of existing gait data and AI models. Meanwhile, a novel quality
evaluation criterion is proposed to quantitatively assess the quality of
existing studies. Through an extensive review and analysis of 164 studies, we
identify and discuss the challenges, potential solutions, and future directions
in this field. Finally, we envision the prospective utilization of 3D skeleton
data for human gait representation and the development of more efficient AI
models for NDs diagnosis. We provide a public resource repository to track and
facilitate developments in this emerging field:
https://github.com/Kali-Hac/AI4NDD-Survey.
