Pedestrian Velocity Estimation via Arm Swing Synchronization
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Solution Overview
Problem
Existing pedestrian velocity estimation methods using GNSS receivers in mobile devices face errors due to power-efficient operation modes, which can lead to inaccurate velocity calculations and increased power consumption.
Innovation Solution
A method that involves extracting a periodicity feature from a sensor signal, such as acceleration magnitude, to initiate pedestrian velocity estimation, synchronizing the GNSS receiver with the user's arm swing to reduce power usage and improve accuracy by determining the fundamental motion frequency and using it to schedule accurate velocity calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the GNSS receiver runs in full power mode continuously, then velocity estimation accuracy is improved, but device power consumption increases
Solution Approach 1:
The system uses periodic sensor signals (e.g., accelerometer data at 50Hz) to detect arm swing cycles and triggers GNSS velocity estimation only at specific phases of the arm swing cycle (e.g., when the arm is at the forward or backward extreme position). This periodic sampling approach captures velocity data at moments when the arm swing bias is minimized, maintaining accuracy while reducing the frequency of full-power GNSS operations compared to continuous sampling.
Solution Approach 2:
The system performs preliminary analysis of sensor signals to detect arm swing periodicity and identify optimal sampling moments before initiating GNSS velocity estimation. By pre-processing accelerometer data to detect zero-crossing events or extreme positions of the arm swing, the system prepares trigger conditions in advance, ensuring that GNSS power is activated only when necessary for accurate measurement, thus avoiding continuous full-power operation.
2Use of energy by moving object
If the GNSS receiver runs in short-dwell power optimization mode, then device power consumption is reduced, but velocity estimation accuracy deteriorates
Solution Approach 1:
The system leverages the periodic nature of arm swing motion by synchronizing GNSS sampling with the detected fundamental motion frequency from sensor data. By sampling velocity at regular intervals corresponding to the arm swing cycle (e.g., every 1-2 seconds for walking), the system achieves accurate velocity estimation without requiring continuous GNSS operation, thus enabling power-efficient short-dwell modes while maintaining measurement precision through strategically timed samples.
Solution Approach 2:
The system uses feedback from sensor signal analysis (accelerometer, gyroscope) to dynamically adjust GNSS sampling timing. By continuously monitoring the periodicity feature extraction from sensor data and using it to trigger GNSS velocity estimation at optimal moments, the system creates a closed-loop control mechanism that ensures accurate velocity capture even in power-optimized modes, preventing accuracy deterioration despite reduced sampling frequency.
3Measurement precision
If velocity estimation is performed continuously, then position tracking accuracy is improved, but unnecessary power consumption occurs during low-motion periods
Solution Approach 1:
The system dynamically adjusts the sampling rate and power state of the GNSS receiver based on real-time analysis of sensor signal periodicity. When the detected fundamental motion frequency indicates low-motion periods or stationary states, the system reduces GNSS sampling frequency or switches to low-power mode, avoiding unnecessary energy consumption. During high-motion periods with clear periodic arm swing patterns, the system increases sampling frequency to maintain position tracking accuracy, creating a dynamic adaptation mechanism that matches power consumption to actual measurement needs.
Data Source
AI summary
Systems, methods and computer-readable mediums are disclosed for GNSS velocity estimation for pedestrians. In some implementations, a method includes receiving a periodic sensor signal; determining a fundamental motion frequency of the periodic sensor signal; extracting a periodicity feature from the periodic sensor signal based on the fundamental motion frequency; and responsive to the extracting, initiating pedestrian velocity estimation.