Wrist Sensor Fusion Enables Robust Gait Quantification Across Walking Scenarios

TL;DR

This study demonstrates that sensor fusion of bilateral wrist accelerometers significantly improves the accuracy of step counting across various walking scenarios, enabling more reliable gait analysis in real-world settings.

Contribution

The paper introduces a sensor fusion approach combining bilateral wrist accelerometer data to enhance step count robustness across diverse walking conditions.

Findings

High-level step fusion improves accuracy in multiple scenarios

Wrist devices can detect steps near toe-off events

Dual-wrist sensor fusion enables robust gait quantification

Abstract

Quantifying step abundance via single wrist-worn accelerometers is a common approach for encouraging active lifestyle and tracking disease status. Nonetheless, step counting accuracy can be hampered by fluctuations in walking pace or demeanor. Here, we assess whether the use of various sensor fusion techniques, each combining bilateral wrist accelerometer data, may increase step count robustness. By collecting data from 27 healthy subjects, we find that high-level step fusion leads to substantially improved accuracy across diverse walking scenarios. Gait cycle analysis illustrates that wrist devices can recurrently detect steps proximal to toe-off events. Collectively, our study suggests that dual-wrist sensor fusion may enable robust gait quantification in free-living environments.