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.

翻译：近年来，全球受神经退行性疾病影响的人口日益增多，传统的医学诊断与监测需要大量的医疗资源和人力投入。作为一种关键的疾病相关运动症状，人类步态可用于表征不同的神经退行性疾病。当前人工智能模型的进展使得基于步态的神经退行性疾病自动识别与分类成为可能，为更快速、更具成本效益的神经退行性疾病诊断开辟了新途径。本文对应用于五种典型神经退行性疾病步态诊断的机器学习和深度学习人工智能技术的最新进展进行了全面综述。我们概述了人工智能辅助神经退行性疾病诊断的流程，并对现有步态数据和人工智能模型进行了系统性分类。同时，本文提出了一种新颖的质量评估标准，用于定量评估现有研究的质量。通过对169项研究的广泛回顾与分析，我们展示了该领域的最新技术进展，讨论了现有挑战、潜在解决方案及未来发展方向。最后，我们展望了利用三维骨骼数据进行人类步态表征以及开发更高效神经退行性疾病诊断人工智能模型的前景。