A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis
Haocong Rao, Minlin Zeng, Xuejiao Zhao, Chunyan Miao
TL;DR
This survey addresses the challenge of diagnosing neurodegenerative diseases (NDs) through human gait, synthesizing 169 studies to reveal how AI—spanning conventional ML, deep learning, and advanced DL—has exploited sensor, vision, and multimodal gait data. It introduces a systematic gait-data and AI-model taxonomy, and a novel quality-evaluation criterion to benchmark study novelty, comparability, and sample sufficiency. A key contribution is the 3D skeleton data vision, including a framework for skeleton-based ND diagnosis that promises more efficient, privacy-preserving gait representations and robust cross-domain learning. The work also maps current data and model limitations (data scarcity, modality gaps, generalizability) and offers concrete directions, including multi-source data integration and 3D skeleton-driven methods, to accelerate clinically impactful gait-based ND diagnosis.
Abstract
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 169 studies, we present recent technical advancements, discuss existing 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.