This paper presents a new method of walking distance estimation using an inertial measurement unit (IMU) placed on the user’s waist. When the sensor is mounted on the waist, the walking steps can be easily detected. However, step length estimation is a challenging task. In this paper, a walking distance estimation method based on histogram features is proposed. The histogram features, including the uniform and logarithmic quantizations, are derived from each step segment of the acceleration norm data. Then, machine learning algorithms such as support vector regression (SVR), gaussian process regression (GPR), and linear regression (LR) are applied based on the histogram features to estimate the walking distance. Two experiments are conducted to evaluate the walking distance estimation accuracy, where test walking paths consist of a straight line corridor (80 m) and a rectangular path (about 1282 m). Experimental results show that the average absolute errors using the SVR model are 0.76% for straight line corridors and 1.14% for the rectangular paths. The proposed method is shown to outperform existing methods from comparison study.

中文翻译：

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使用腰部安装 IMU 的基于直方图特征的步行距离估计方法

本文提出了一种使用放置在用户腰部的惯性测量单元 (IMU) 来估计步行距离的新方法。当传感器安装在腰部时，可以很容易地检测到步行的步数。然而，步长估计是一项具有挑战性的任务。本文提出了一种基于直方图特征的步行距离估计方法。直方图特征，包括均匀量化和对数量化，是从加速度范数数据的每个步长段中导出的。然后，基于直方图特征应用支持向量回归（SVR）、高斯过程回归（GPR）和线性回归（LR）等机器学习算法来估计步行距离。进行了两个实验来评估步行距离估计的准确性，其中测试步行路径由直线走廊（80 m）和矩形路径（约1282 m）组成。实验结果表明，使用 SVR 模型的平均绝对误差对于直线走廊为 0.76%，对于矩形路径为 1.14%。从比较研究中可以看出，所提出的方法优于现有方法。