Stride time variability is an important indicator for the assessment of gait stability. An accurate extraction of the stride intervals is essential for determining stride time variability. Peak detection is a commonly used method for gait segmentation and stride time estimation. Standard peak detection algorithms often fail due to additional movement components and measurement noise. A novel algorithm for robust peak detection in inertial sensor signals was proposed in a previous contribution. In this work, we present a novel approach for estimation of stride time variability based on the formerly proposed peak detection algorithm applied to an unobtrusive sensor setup for motion monitoring. The unobtrusive sensor setup includes a wrist sensor, a pocket or belt sensor, and a necklace sensor, all equipped with both accelerometer and gyroscope. The goal of this work is to implement a generalized approach for accurate and robust stride interval determining algorithm for different sensor locations. Therefore, treadmill and level ground walking experiments were conducted with ten healthy subjects at increasing walking speeds and an age-simulating suit. With the proposed algorithm, we achieved a RMSE of 0.07 s for the stride interval estimation during treadmill walking experiments. The results give promising indications that detection of variation of stride time variability is possible using the proposed unobtrusive sensor setup.

中文翻译：

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使用惯性测量传感器进行不干扰的长期监测中步幅时间变异性的估算。

步幅时间可变性是评估步态稳定性的重要指标。准确地提取步幅间隔对于确定步幅时间可变性至关重要。峰值检测是步态分割和步幅时间估计的常用方法。由于其他运动分量和测量噪声，标准峰检测算法通常会失败。在先前的贡献中，提出了一种用于惯性传感器信号中鲁棒峰值检测的新算法。在这项工作中，我们提出了一种基于先前提出的峰值检测算法来估算步幅时间变化性的新方法，该算法应用于运动监测的非干扰性传感器设置。不显眼的传感器设置包括腕部传感器，口袋或皮带传感器和项链传感器，所有传感器均配备了加速度计和陀螺仪。这项工作的目标是为不同传感器位置的准确而鲁棒的步幅间隔确定算法实现一种通用方法。因此，对十名健康受试者以递增的步行速度和模拟年龄的衣服进行了跑步机和水平地面步行实验。使用提出的算法，我们在跑步机步行实验期间获得了0.07 s的RMSE，用于步距估计。结果提供了有前途的迹象，表明使用建议的无干扰传感器设置可以检测步幅时间可变性的变化。跑步机和水平地面步行实验是针对十名健康受试者以递增的步行速度和模拟年龄的衣服进行的。使用提出的算法，我们在跑步机步行实验期间获得了0.07 s的RMSE，用于步距估计。结果提供了有前途的迹象，表明使用建议的无干扰传感器设置可以检测步幅时间可变性的变化。跑步机和水平地面步行实验是针对十名健康受试者以递增的步行速度和模拟年龄的衣服进行的。使用提出的算法，我们在跑步机步行实验期间获得了0.07 s的RMSE，用于步距估计。结果提供了有希望的迹象，表明使用建议的无干扰传感器设置可以检测步幅时间可变性的变化。