# Zero-Velocity Detection - A Bayesian Approach to Adaptive Thresholding

## TL;DR

This paper introduces a Bayesian zero-velocity detection method that adaptively adjusts thresholds based on motion context, improving inertial navigation accuracy across different gait speeds.

## Contribution

It extends existing likelihood-ratio detectors by incorporating prior information and cost models, enabling adaptive thresholding in zero-velocity detection.

## Key findings

- Outperforms fixed-threshold detectors in various gait speeds
- Effectively incorporates prior velocity estimates and cost considerations
- Enhances zero-velocity detection accuracy in inertial navigation

## Abstract

A Bayesian zero-velocity detector for foot-mounted inertial navigation systems is presented. The detector extends existing zero-velocity detectors based on the likelihood-ratio test, and allows, possibly time-dependent, prior information about the two hypotheses - the sensors being stationary or in motion - to be incorporated into the test. It is also possible to incorporate information about the cost of a missed detection or a false alarm. Specifically, we consider an hypothesis prior based on the velocity estimates provided by the navigation system and an exponential model for how the cost of a missed detection increases with the time since the last zero-velocity update. Thereby, we obtain a detection threshold that adapts to the motion characteristics of the user. Thus, the proposed detection framework efficiently solves one of the key challenges in current zero-velocity-aided inertial navigation systems: the tuning of the zero-velocity detection threshold. A performance evaluation on data with normal and fast gait demonstrates that the proposed detection framework outperforms any detector that chooses two separate fixed thresholds for the two gait speeds.