Abstract

Gait analysis has been studied for a long time and applied to fields such as security, sport, and medicine. In particular, clinical gait analysis has played a significant role in improving the quality of healthcare. With the growth of machine learning technology in recent years, deep learning-based approaches to gait analysis have become popular. However, a large number of samples are required for training models when using deep learning, where the amount of available gait-related data may be limited for several reasons. This paper discusses certain techniques that can be applied to enable the use of deep learning for gait analysis in case of limited availability of data. Recent studies on the clinical applications of deep learning for gait analysis are also reviewed, and the compatibility between these applications and sensing modalities is determined. This article also provides a broad overview of publicly available gait databases for different sensing modalities.

中文翻译：

---

基于步态的深度学习技术在临床应用中的近期应用：一项调查

摘要

步态分析已经被研究了很长时间，并应用于安全、运动和医学等领域。特别是，临床步态分析在提高医疗质量方面发挥了重要作用。近年来，随着机器学习技术的发展，基于深度学习的步态分析方法变得流行起来。然而，在使用深度学习时，训练模型需要大量样本，其中可用的步态相关数据量可能由于多种原因而受到限制。本文讨论了可用于在数据有限的情况下使用深度学习进行步态分析的某些技术。还回顾了最近关于深度学习在步态分析中的临床应用的研究，并确定这些应用程序和传感模式之间的兼容性。本文还提供了针对不同传感模式的公开可用步态数据库的广泛概述。