利用惯性测量单元自动检测步态事件:健康受试者和中度至重度障碍患者。

IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Cyril Voisard, Nicolas de l'Escalopier, Damien Ricard, Laurent Oudre
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引用次数: 0

摘要

背景:最近,惯性测量单元(IMU)在步态定量分析中的应用在临床实践中得到了广泛发展。自动检测步态事件(GEs)的方法层出不穷。虽然其中许多方法在健康受试者中达到了很高的效率,但在中度到重度障碍患者的高度退化步态中检测 GEs 仍然是一项挑战。在本文中,我们旨在介绍一种在这种情况下从 IMU 记录中改进 GE 检测的方法:我们记录了 13 名健康受试者、29 名多发性硬化症患者和 21 名中风后马蹄内翻足患者的 10 米步态 IMU 信号。仪器垫被用作金标准。我们的方法是从过滤后的加速度信号(不含重力和回旋信号)中检测 GE。首先,我们使用自相关和模式检测技术来识别参考步幅模式。其次,我们应用多参数动态时间扭曲技术,从模型步幅中注释该模式,以检测信号中的所有 GE:我们分析了健康受试者记录的 16 819 个 GE,F1 分数达到 100%,中位绝对误差为 8 毫秒(IQR [3-13] 毫秒)。在多发性硬化症和马蹄内翻足队列中,我们分别分析了 6067 和 8951 个 GE,F1 分数分别为 99.4% 和 96.3%,中位绝对误差分别为 18 毫秒(IQR [8-39] 毫秒)和 26 毫秒(IQR [12-50] 毫秒):我们的结果与健康人的技术水平一致,并证明了病理患者 GEs 检测的良好准确性。因此,我们提出的方法提供了一种从 IMU 信号检测 GEs 的有效方法,即使在退化的步态中也是如此。不过,该方法在使用前应在每个队列中进行评估,以确保其可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients.

Background: Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases.

Methods: We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal.

Results: We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms).

Conclusions: Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.

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来源期刊
Journal of NeuroEngineering and Rehabilitation
Journal of NeuroEngineering and Rehabilitation 工程技术-工程:生物医学
CiteScore
9.60
自引率
3.90%
发文量
122
审稿时长
24 months
期刊介绍: Journal of NeuroEngineering and Rehabilitation considers manuscripts on all aspects of research that result from cross-fertilization of the fields of neuroscience, biomedical engineering, and physical medicine & rehabilitation.
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