Pedestrian Dead Reckoning Velocity Estimation for Mobile Device Placements
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Solution Overview
Problem
Pedestrian Dead Reckoning (PDR) systems face challenges in accurately estimating pedestrian velocity and direction of travel across various device placements, such as on the wrist, body, or in-hand, due to differences in motion signatures and the need for robust classification and estimation methods.
Innovation Solution
The system employs inertial sensors to obtain motion data, classifies device placement, and uses kinematic models and neural networks to estimate velocity and direction of travel, integrating multiple sources and uncertainties to provide accurate PDR estimates, while accounting for device orientation and user activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If PDR estimation is performed for multiple device placements, then the versatility and adaptability of the system is improved, but the device complexity and difficulty of detecting and measuring motion signatures increase
Solution Approach 1:
The system segments the PDR estimation process into separate modules: device placement classification module, pedestrian/non-pedestrian classification module, direction of travel estimation module, and velocity estimation module. Each module handles a specific aspect of the complex task independently, making the overall system more manageable and adaptable to different device placements without requiring complete redesign.
Solution Approach 2:
The system implements a universal PDR estimation framework that can handle multiple device placements (wrist, body, in-hand, leg pocket) using the same core components. The placement classification module identifies the specific device placement and routes the motion data to appropriate estimation algorithms, allowing one system to serve multiple functions across different scenarios.
2Measurement precision
If multiple direction of travel sources are integrated, then the measurement precision of velocity estimation is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The system merges multiple direction of travel sources (arm-swing based estimation, leg-swing based estimation, and body motion based estimation) into a unified velocity estimation process. The direction of travel estimation module combines these sources and the velocity estimation module integrates them with speed estimates to produce a final velocity vector, achieving higher precision through consolidation rather than separate processing.
Solution Approach 2:
The velocity estimation module acts as an intermediary that processes and combines outputs from multiple direction of travel sources and speed estimation modules. It mediates between the raw sensor data and the final velocity output, performing the necessary integration and weighting to achieve precise velocity estimation while managing the complexity of multiple sources.
3Reliability
If device placement classification is performed, then the reliability of PDR estimation is improved, but the processing time and computational requirements increase
Solution Approach 1:
The system performs device placement classification as a preliminary action before the main PDR estimation process. By determining the device placement (wrist, body, in-hand, leg pocket) upfront, the system can select the most appropriate estimation algorithms in advance, avoiding unnecessary processing steps and reducing overall computation time while maintaining high reliability through accurate placement identification.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables reliable PDR estimation across diverse device placements, reducing position errors and drift, and enhancing navigation, health monitoring, and spatial audio applications by providing precise velocity and position data.
Implementation Method 1
obtaining motion data from a motion sensor of a mobile device
Data Source
AI summary
Embodiments are disclosed for PDR for different device placements. In some embodiments, a method comprises: obtaining motion data from a motion sensor of a mobile device; determining pedestrian or non-pedestrian class based on the device motion data; determining a placement of the device based on the device motion data; estimating a first direction of travel based on multiple direction of travel sources and the device placement; estimating a first velocity based on the pedestrian or non-pedestrian classification, the first estimate of direction of travel and a first estimate of speed; estimating a second velocity based on a kinematic model and the device motion data; selecting the first estimated velocity or the second estimated velocity based on selection logic; and determining a relative position of the device based on the selected estimated velocity.