A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis

2024-05-21

arXiv.org

AI Summary

This study addresses the low efficiency and high cost of early diagnosis for neurodegenerative diseases (NDs). We systematically reviewed 169 AI-driven gait analysis studies and established a comprehensive gait data–AI model taxonomy covering five major NDs, including Parkinson’s and Alzheimer’s diseases. We introduced the first quantitative assessment framework for study quality, grounded in methodological rigor and clinical applicability, and proposed a novel direction integrating 3D skeletal representation with lightweight AI models. Key bottlenecks were identified: high inter-study data heterogeneity, poor model generalizability, and insufficient interpretability. To address these, we advocate explainable modeling approaches and multicenter collaborative validation. The work provides both a theoretical framework and practical guidelines to enhance the clinical translation efficacy of gait-based AI diagnostics.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Humans and AI: Other Foundations of Human Computation & AI
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• AI for gait-based neurodegenerative disease diagnosis
• Survey of machine and deep learning techniques
• Quality evaluation of AI models in NDs

Innovation

• AI models for gait analysis
• ML and DL in NDs diagnosis
• 3D skeleton data utilization