Robust Data-Driven Zero-Velocity Detection for Foot-Mounted Inertial Navigation

TL;DR

This paper introduces two innovative zero-velocity detection methods for foot-mounted inertial navigation, utilizing adaptive thresholds and LSTM neural networks, resulting in improved accuracy across various motions.

Contribution

The paper presents two novel zero-velocity detection techniques, one with adaptive thresholding and another using LSTM, enhancing robustness and accuracy over existing methods.

Findings

Both detectors outperform existing methods in accuracy.

The LSTM model generalizes across different sensors with data augmentation.

The adaptive threshold method improves detection in diverse motion scenarios.

Abstract

We present two novel techniques for detecting zero-velocity events to improve foot-mounted inertial navigation. Our first technique augments a classical zero-velocity detector by incorporating a motion classifier that adaptively updates the detector's threshold parameter. Our second technique uses a long short-term memory (LSTM) recurrent neural network to classify zero-velocity events from raw inertial data, in contrast to the majority of zero-velocity detection methods that rely on basic statistical hypothesis testing. We demonstrate that both of our proposed detectors achieve higher accuracies than existing detectors for trajectories including walking, running, and stair-climbing motions. Additionally, we present a straightforward data augmentation method that is able to extend the LSTM-based model to different inertial sensors without the need to collect new training data.