Prisco Giuseppe, Pirozzi Maria Agnese, Santone Antonella, Esposito Fabrizio, Cesarelli Mario, Amato Francesco, Donisi Leandro
Department of Medicine and Health Sciences, University of Molise, 86100 Campobasso, Italy.
Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, 80138 Naples, Italy.
Diagnostics (Basel). 2024 Dec 27;15(1):36. doi: 10.3390/diagnostics15010036.
: Gait analysis, traditionally performed with lab-based optical motion capture systems, offers high accuracy but is costly and impractical for real-world use. Wearable technologies, especially inertial measurement units (IMUs), enable portable and accessible assessments outside the lab, though challenges with sensor placement, signal selection, and algorithm design can affect accuracy. This systematic review aims to bridge the benchmarking gap between IMU-based and traditional systems, validating the use of wearable inertial systems for gait analysis. : This review examined English studies between 2012 and 2023, retrieved from the Scopus database, comparing wearable sensors to optical motion capture systems, focusing on IMU body placement, gait parameters, and validation metrics. Exclusion criteria for the search included conference papers, reviews, unavailable papers, studies without wearable inertial sensors for gait analysis, and those not involving agreement studies or optical motion capture systems. : From an initial pool of 479 articles, 32 were selected for full-text screening. Among them, the lower body resulted in the most common site for single IMU placement (in 22 studies), while the most frequently used multi-sensor configuration involved IMU positioning on the lower back, shanks, feet, and thighs (10 studies). Regarding gait parameters, 11 studies out of the 32 included studies focused on spatial-temporal parameters, 12 on joint kinematics, 2 on gait events, and the remainder on a combination of parameters. In terms of validation metrics, 24 studies employed correlation coefficients as the primary measure, while 7 studies used a combination of error metrics, correlation coefficients, and Bland-Altman analysis. Validation metrics revealed that IMUs exhibited good to moderate agreement with optical motion capture systems for kinematic measures. In contrast, spatiotemporal parameters demonstrated greater variability, with agreement ranging from moderate to poor. : This review highlighted the transformative potential of wearable IMUs in advancing gait analysis beyond the constraints of traditional laboratory-based systems.
传统上，步态分析是通过基于实验室的光学运动捕捉系统进行的，其精度很高，但成本高昂且在实际应用中不切实际。可穿戴技术，尤其是惯性测量单元（IMU），能够在实验室外进行便携式且易于获取的评估，不过传感器放置、信号选择和算法设计方面的挑战可能会影响准确性。本系统综述旨在弥合基于IMU的系统与传统系统之间的基准差距，验证可穿戴惯性系统在步态分析中的应用。：本综述考察了2012年至2023年间从Scopus数据库中检索到的英文研究，将可穿戴传感器与光学运动捕捉系统进行比较，重点关注IMU在身体上的放置、步态参数和验证指标。搜索的排除标准包括会议论文、综述、无法获取的论文、没有用于步态分析的可穿戴惯性传感器的研究，以及那些不涉及一致性研究或光学运动捕捉系统的研究。：从最初的479篇文章中，筛选出32篇进行全文审查。其中，下半身是单个IMU放置最常见的部位（22项研究），而最常用的多传感器配置是将IMU放置在下背部、小腿、足部和大腿上（10项研究）。关于步态参数，32项纳入研究中有11项关注时空参数，12项关注关节运动学，2项关注步态事件，其余关注参数组合。在验证指标方面，24项研究采用相关系数作为主要测量方法，7项研究使用误差指标、相关系数和布兰德-奥特曼分析的组合。验证指标显示，对于运动学测量，IMU与光学运动捕捉系统表现出良好到中等程度的一致性。相比之下，时空参数表现出更大的变异性，一致性从中等到较差不等。：本综述强调了可穿戴IMU在突破传统基于实验室的系统的限制推进步态分析方面的变革潜力。
