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The development and concurrent validity of a real-time algorithm for temporal gait analysis using inertial measurement units

机译:惯性测量单元的实时算法实时算法的开发及并发有效性

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The use of inertial measurement units (IMUs) for gait analysis has emerged as a tool for clinical applications. Shank gyroscope signals have been utilized to identify heel-strike and toe-off, which serve as the foundation for calculating temporal parameters of gait such as single and double limb support time. Recent publications have shown that toe-off occurs later than predicted by the dual minima method (DMM), which has been adopted as an IMU-based gait event detection algorithm. In this study, a real-time algorithm, Noise-Zero Crossing (NZC), was developed to accurately compute temporal gait parameters. Our objective was to determine the concurrent validity of temporal gait parameters derived from the NZC algorithm against parameters measured by an instrumented walkway. The accuracy and precision of temporal gait parameters derived using NZC were compared to those derived using the DMM. The results from Bland-Altman Analysis showed that the NZC algorithm had excellent agreement with the instrumented walkway for identifying the temporal gait parameters of Gait Cycle Time (GCT), Single Limb Support (SLS) time, and Double Limb Support (DLS) time. By utilizing the moment of zero shank angular velocity to identify toe-off, the NZC algorithm performed better than the DMM algorithm in measuring SLS and DLS times. Utilizing the NZC algorithm's gait event detection preserves DLS time, which has significant clinical implications for pathologic gait assessment. (C) 2017 Elsevier Ltd. All rights reserved.
机译:使用惯性测量单元(IMU)的步态分析是临床应用的工具。 Shank陀螺仪信号已被利用来识别脚后跟和脚趾,这是计算单个和双肢支撑时间等步态的时间参数的基础。最近的出版物表明,由于双重最小方法(DMM)预测,脚趾发生,这已被采用作为基于IMU的步态事件检测算法。在该研究中,开发了一种实时算法,噪声零交叉(NZC),以准确计算时间步态参数。我们的目的是确定从NZC算法的时间步态参数对由仪表式走道测量的参数衍生的时间步态参数的并行有效性。将使用NZC的时间步态参数的准确性和精度与使用DMM衍生的那些。 Bland-Altman分析的结果表明,NZC算法与仪表走道的良好协议,用于识别步态周期时间(GCT),单肢支持(SLS)时间和双肢支持(DLS)时间的时间步态参数。通过利用零柄角速度的时刻来识别托管,NZC算法比测量SLS和DLS时间更好地执行DMM算法。利用NZC算法的步态事件检测保留了DLS时间,这对病理步态评估具有显着的临床意义。 (c)2017 Elsevier Ltd.保留所有权利。

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