High-Performance Stacked Ensemble Model for Stride Length Estimation with Potential Application in Indoor Positioning Systems

Abstract

The pedestrian dead reckoning (PDR) infrastructure-independent positioning method is of particular interest for many researchers due to its effectiveness in indoor positioning systems. The PDR technique consists of three main components including stride detection, movement heading estimation and stride length estimation (SLE). Among those, SLE results can be utilized in numerous applications, such as indoor positioning, disease diagnosis, incident detecting for patient warning, etc.. Traditional solutions for estimating stride length usually have simple structures with short calculation time but they do not ensure the expected accuracy as desired. To enhance the estimated stride length, recently, many solutions employing deep learning based techniques such as Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), have been developed to improve the SLE result. Although the accuracy in SLE has been improved compared to traditional methods, these deep learning based solutions still remain some limitations, such as complex structure and huge training parameters. This paper proposes some solutions of using machine learning based stacked ensemble model and simple machine learning algorithm to improve the accuracy of SLE while satisfying requirements such as simple structures, short execution time. The experimental results on real field data show that the proposed approaches outperform other state-of-the-art deep learning based methods on both SLE accuracy and computation time by at least 12% more accurate and seven times faster, respectively.