Frailty is a significant issue in geriatric health, may cause adverse effects such as falls, delirium, weight loss, or physical decline. Over time, various methods have been developed for measuring frailty, including clinical judgment, the frailty index, the clinical frailty scale, and the comprehensive geriatric assessment. These traditional frailty assessment approaches rely on healthcare professionals, which can lead to inaccuracy and require frequent clinic visits, making it burdensome for elderly patients. This review paper explores the latest trends in frailty assessment by measuring gait parameters using wearable sensors, specifically the inertial measurement unit (IMU). The aim of this study is to provide a comprehensive overview of objective methods for evaluating and quantifying frailty. We focus on the application of machine learning (ML) and deep learning (DL) techniques to IMU gait data, highlighting key aspects of recent publications such as algorithms, sensor types, sample sizes, and performance evaluations. By examining the strengths and challenges of each technique, this review aims to guide future studies on utilizing cost‐effective and portable devices integrated with clinical data. This integration can help to propose optimized IMU gait parameters or ML models to detect early‐stage frailty. This advances the emerging trend of intelligent, individualized, and efficient healthcare systems for older adults.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Artificial Intelligence

中文翻译：

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使用基于惯性测量传感器的步态参数测量进行衰弱评估的演变：详细分析


虚弱是老年人健康的一个重要问题，可能会导致跌倒、谵妄、体重减轻或身体衰退等不良影响。随着时间的推移，已经开发出多种测量衰弱的方法，包括临床判断、衰弱指数、临床衰弱量表和综合老年评估。这些传统的衰弱评估方法依赖于医疗保健专业人员，这可能会导致不准确，并且需要频繁就诊，给老年患者带来负担。这篇综述论文通过使用可穿戴传感器，特别是惯性测量单元（IMU）测量步态参数，探讨了衰弱评估的最新趋势。本研究的目的是全面概述评估和量化衰弱的客观方法。我们专注于机器学习 (ML) 和深度学习 (DL) 技术在 IMU 步态数据中的应用，重点介绍最新出版物的关键方面，例如算法、传感器类型、样本大小和性能评估。通过研究每种技术的优势和挑战，本综述旨在指导未来关于利用与临床数据集成的经济高效的便携式设备的研究。这种集成有助于提出优化的 IMU 步态参数或 ML 模型来检测早期虚弱。这推动了老年人智能、个性化、高效医疗保健系统的新兴趋势。本文分类如下：应用领域 > 医疗保健技术 > 机器学习技术 > 人工智能