Department of Telecommunications and Information Processing-IMEC, Ghent University, 9000 Gent, Belgium.
Sensors (Basel). 2020 Jan 31;20(3):778. doi: 10.3390/s20030778.
An accurate step length estimation can provide valuable information to different applications such as indoor positioning systems or it can be helpful when analyzing the gait of a user, which can then be used to detect various gait impairments that lead to a reduced step length (caused by e.g., Parkinson's disease or multiple sclerosis). In this paper, we focus on the estimation of the step length using machine learning techniques that could be used in an indoor positioning system. Previous step length algorithms tried to model the length of a step based on measurements from the accelerometer and some tuneable (user-specific) parameters. Machine-learning-based step length estimation algorithms eliminate these parameters to be tuned. Instead, to adapt these algorithms to different users, it suffices to provide examples of the length of multiple steps for different persons to the machine learning algorithm, so that in the training phase the algorithm can learn to predict the step length for different users. Until now, these machine learning algorithms were trained with features that were chosen intuitively. In this paper, we consider a systematic feature selection algorithm to be able to determine the features from a large collection of features, resulting in the best performance. This resulted in a step length estimator with a mean absolute error of 3.48 cm for a known test person and 4.19 cm for an unknown test person, while current state-of-the-art machine-learning-based step length estimators resulted in a mean absolute error of 4.94 cm and 6.27 cm for respectively a known and unknown test person.
准确的步长估计可以为不同的应用提供有价值的信息，例如室内定位系统，或者在分析用户步态时也很有帮助，因为这可以用于检测各种导致步长减小的步态障碍（例如帕金森病或多发性硬化症）。在本文中，我们专注于使用机器学习技术来估计步长，这些技术可用于室内定位系统。以前的步长算法试图根据加速度计的测量值和一些可调节（用户特定）参数来建模步长的长度。基于机器学习的步长估计算法消除了这些需要调整的参数。相反，为了使这些算法适应不同的用户，只需向机器学习算法提供不同人多次步长的示例，以便在训练阶段，算法可以学习为不同的用户预测步长。到目前为止，这些机器学习算法都是使用直观选择的特征进行训练的。在本文中，我们考虑使用系统的特征选择算法，以便能够从大量特征中确定最佳性能的特征。这使得步长估计器对于已知测试者的平均绝对误差为 3.48 厘米，对于未知测试者的平均绝对误差为 4.19 厘米，而当前基于机器学习的最先进的步长估计器对于已知和未知测试者的平均绝对误差分别为 4.94 厘米和 6.27 厘米。
