SMART Engineering Solutions & Technologies (SMARTEST) Research Center, Università Telematica "Ecampus", Novedrate, Comune, Italy.
Department of Psychology, Sapienza University, Rome, Italy.
Expert Rev Med Devices. 2021 Dec;18(sup1):79-94. doi: 10.1080/17434440.2021.1988849. Epub 2021 Oct 12.
The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings.
Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings.
IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.
本综述的目的是追踪可穿戴式 IMU 的发展历程,从其在监督性实验室和基于日常活动的设置中的应用,到在无人监督的自然环境中对人体运动进行长期监测的应用。
确定并综合了四个主要的新兴应用领域,即移动健康解决方案(具体用于评估虚弱、跌倒风险、慢性神经疾病以及监测和促进积极生活)、职业工效学、康复和远程康复以及认知评估。从这些领域的近期科学文献中综合了从应用和/或临床角度的研究结果,目的是为临床研究人员和从业者提供有关惯性传感器在应用临床环境中的现代用途的实用指导。
基于 IMU 的可穿戴设备已经从实验室临床实践迅速过渡到无人监督的应用环境。要成功地将可穿戴式惯性传感器用于评估应用环境中的移动性、运动表现和运动障碍,还需要机器学习算法来管理这些传感器产生的大量数据,以提取出对从业者具有临床相关性和可解释性的信息。