Gait analysis system for assessing abnormal patterns in individuals with hemiparetic stroke during robot-assisted gait training: a criterion-related validity study in healthy adults.

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Neurorobotics Pub Date : 2025-05-21 eCollection Date: 2025-01-01 DOI:10.3389/fnbot.2025.1558009
Issei Nakashima, Daisuke Imoto, Satoshi Hirano, Hitoshi Konosu, Yohei Otaka
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引用次数: 0

Abstract

Introduction: Gait robots have the potential to analyze gait characteristics during gait training using mounted sensors in addition to robotic assistance of the individual's movements. However, no systems have been proposed to analyze gait performance during robot-assisted gait training. Our newly developed gait robot," Welwalk WW-2000 (WW-2000)" is equipped with a gait analysis system to analyze abnormal gait patterns during robot-assisted gait training. We previously investigated the validity of the index values for the nine abnormal gait patterns. Here, we proposed new index values for four abnormal gait patterns, which are anterior trunk tilt, excessive trunk shifts over the affected side, excessive knee joint flexion, and swing difficulty; we investigated the criterion validity of the WW-2000 gait analysis system in healthy adults for these new index values.

Methods: Twelve healthy participants simulated four abnormal gait patterns manifested in individuals with hemiparetic stroke while wearing the robot. Each participant was instructed to perform 16 gait trials, with four grades of severity for each of the four abnormal gait patterns. Twenty strides were recorded for each gait trial using a gait analysis system in the WW-2000 and video cameras. Abnormal gait patterns were assessed using the two parameters: the index values calculated for each stride from the WW-2000 gait analysis system, and assessor's severity scores for each stride. The correlation of the index values between the two methods was evaluated using the Spearman rank correlation coefficient for each gait pattern in each participant.

Results: The median (minimum to maximum) values of Spearman rank correlation coefficient among the 12 participants between the index value calculated using the WW-2000 gait analysis system and the assessor's severity scores for anterior trunk tilt, excessive trunk shifts over the affected side, excessive knee joint flexion, and swing difficulty were 0.892 (0.749-0.969), 0.859 (0.439-0.923), 0.920 (0.738-0.969), and 0.681 (0.391-0.889), respectively.

Discussion: The WW-2000 gait analysis system captured four new abnormal gait patterns observed in individuals with hemiparetic stroke with high validity, in addition to nine previously validated abnormal gait patterns. Assessing abnormal gait patterns is important as improving them contributes to stroke rehabilitation.

Clinical trial registration: https://jrct.niph.go.jp, identifier jRCT 042190109.

步态分析系统在机器人辅助的步态训练中评估偏瘫中风患者的异常模式:一项健康成人标准相关的有效性研究。
步态机器人在步态训练过程中,除了机器人辅助个人运动外,还可以使用安装的传感器分析步态特征。然而,在机器人辅助的步态训练过程中,还没有提出分析步态性能的系统。我们新开发的步态机器人Welwalk www -2000 (www -2000)配备了步态分析系统,用于分析机器人辅助步态训练过程中的异常步态模式。我们之前调查了九种异常步态模式的指标值的有效性。在这里,我们提出了四种异常步态模式的新指标值,这四种异常步态模式是躯干前倾、躯干过度向患侧移动、膝关节过度屈曲和摇摆困难;我们调查了WW-2000步态分析系统在健康成人中对这些新指标值的效度。方法:12名健康参与者在佩戴机器人时模拟了偏瘫中风患者的四种异常步态模式。每位参与者被要求进行16次步态试验,每种步态异常模式的严重程度分为四个等级。使用WW-2000中的步态分析系统和摄像机记录每次步态试验的20步。采用WW-2000步态分析系统计算的每一步的指数值和评估者对每一步的严重程度评分两个参数对异常步态模式进行评估。使用Spearman秩相关系数对每个参与者的每种步态模式评估两种方法之间指标值的相关性。结果:12名受试者中,采用WW-2000步态分析系统计算的指数值与评估者前肢倾斜、躯干过度向患侧移动、膝关节过度屈曲、摇摆困难程度评分的Spearman秩相关系数中位数(最小至最大值)分别为0.892(0.749-0.969)、0.859(0.439-0.923)、0.920(0.738-0.969)、0.681(0.391-0.889)。讨论:WW-2000步态分析系统捕获了四种新的异常步态模式,在偏瘫中风患者中观察到高效度,除了先前验证的九种异常步态模式。评估异常的步态模式是重要的,因为改善它们有助于中风康复。临床试验注册:https://jrct.niph.go.jp,编号jRCT 042190109。
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来源期刊
Frontiers in Neurorobotics
Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS
CiteScore
5.20
自引率
6.50%
发文量
250
审稿时长
14 weeks
期刊介绍: Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
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