A Novel Zero-Velocity Interval Detection Algorithm for a Pedestrian Navigation System with Foot-Mounted Inertial...

Area of Science:

• Inertial Navigation
• Signal Processing
• Human Motion Analysis

Background:

• The Zero-Velocity Update (ZUPT) algorithm is crucial for pedestrian navigation accuracy using inertial sensors.
• Accurate identification of zero-velocity intervals during human locomotion is a primary challenge for ZUPT.
• Existing methods often struggle with varying gait patterns and motion states.

Purpose of the Study:

• To develop an innovative adaptive sliding window technique for precise zero-velocity detection.
• To enhance the accuracy and robustness of the ZUPT algorithm for pedestrian navigation.
• To adapt the zero-velocity detection threshold based on the pedestrian's gait frequency.

Main Methods:

• Utilized Fourier Transform to isolate pedestrian gait frequency from spectral data.
• Implemented an adaptive sliding window technique for gait frequency analysis.
• Dynamically adjusted the zero-velocity detection threshold according to the identified gait frequency.

Main Results:

• The proposed adaptive method significantly improved the precision of zero-velocity interval detection.
• Outperformed traditional fixed-threshold approaches by minimizing false positives.
• Demonstrated adaptability to various motion states including slow, fast, and running gaits.
• Substantially improved pedestrian trajectory localization accuracy under diverse walking conditions.

Conclusions:

• The novel adaptive algorithm enhances ZUPT performance through accurate gait frequency identification and adaptive thresholding.
• This method offers a significant advancement for reliable and precise pedestrian navigation systems.
• The technique shows strong potential for real-world applications in pedestrian tracking and navigation.