An inertial navigation framework using mixture-of-experts models and bicycle pedaling constraints improves tracking accuracy by at least 12% over baselines in GNSS-blocked environments, with wheel speed errors below 0.5 m/s at the 95th percentile.
Threshold-free phase segmentation and zero velocity detection for gait analysis using foot-mounted inertial sensors
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Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments
An inertial navigation framework using mixture-of-experts models and bicycle pedaling constraints improves tracking accuracy by at least 12% over baselines in GNSS-blocked environments, with wheel speed errors below 0.5 m/s at the 95th percentile.