Nonlinear Step Length Estimation Model for Pedestrian Navigation
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Solution Overview
Problem
Existing methods for estimating step length during human movement, such as walking or running, face challenges due to user dependency, accuracy issues, and neglect of nonlinear motion dynamics, particularly when considering varying speeds and individual differences in physical characteristics.
Innovation Solution
A method and system utilizing a nonlinear system identification technique, specifically Fast Orthogonal Search (FOS), to build a model for estimating step length based on parameters representing human motion dynamics, which can be applied universally across different users and motion speeds, using sensors like accelerometers and gyroscopes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If linear models are used to estimate step length based on step frequency and acceleration variance, then the model is simple and easy to implement, but the accuracy is insufficient because linear relationships cannot capture nonlinear motion dynamics
Solution Approach 1:
The patent transforms the linear model parameters into nonlinear functions of motion dynamics parameters. Specifically, the step length is modeled as a nonlinear function of step frequency and acceleration variance, capturing the complex relationships that linear models cannot represent. This allows the model to adapt to varying walking and running conditions while maintaining computational efficiency.
2Measurement precision
If user-specific calibration is performed to account for individual physical characteristics, then the estimation accuracy for each user improves, but the system becomes user-dependent and requires customization
Solution Approach 1:
The patent develops a universal nonlinear model that works across different users without requiring individual calibration. The model uses motion dynamics parameters (step frequency and acceleration variance) that naturally capture individual characteristics, making the system applicable to all users while maintaining high accuracy. This eliminates the need for user-specific customization while preserving adaptability.
3Adaptability or versatility
If double integration of acceleration readings is used to obtain pedestrian displacement, then the method can be applied without user calibration, but the accuracy deteriorates due to drift increasing over time and high noise in accelerometer readings
Solution Approach 1:
The patent extracts only the necessary information for step length estimation from the acceleration data, avoiding full double integration. By focusing on step frequency and acceleration variance as intermediate parameters, the method eliminates the accumulation of drift errors that occur during double integration while still providing accurate displacement estimates for step length calculation.
Data Source
AI summary
The present disclosure relates to a method and system for estimating varying step length for on foot motion (such as for example walking or running). The present method and apparatus is able to be used in anyone or both of two different phases depending on the embodiment. The first phase is a model-building phase done offline to obtain the nonlinear model for the step length as a function of different parameters that represent human motion dynamics, the model is built using a nonlinear system identification technique. In the second phase the nonlinear model is used to calculate the step length from the different parameters that represent human motion dynamics used as input to the model. These parameters are obtained from sensors readings from the sensors in the apparatus. This second phase is the more frequent usage of the present method and apparatus for a variety of applications.