Wearable Attitude Matrix Calculation Using Gyroscope Data
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Solution Overview
Problem
Existing attitude matrix calculation methods for wearable devices require complex convergence and matrix inversion, leading to high power consumption and memory usage, especially when calculating motion distance based on tri-axis gyroscope, accelerometer, and magnetometer data.
Innovation Solution
A method that calculates the attitude matrix using only real-time gyroscope data, eliminating the need for accelerometer and magnetometer measurements, and employs a constant transformation matrix between the sensor and foot coordinate systems to reduce computational complexity and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex convergence algorithms (unscented Kalman filter or extended Kalman filter) are used to calculate attitude matrix from tri-axis gyroscope, accelerometer, and magnetometer data, then measurement precision and reliability are improved, but device complexity, power consumption, and memory usage increase
Solution Approach 1:
The patent extracts and removes the accelerometer and magnetometer from the attitude matrix calculation process, using only gyroscope data. This eliminates the need for complex multi-sensor convergence algorithms while maintaining acceptable measurement precision for motion distance calculation.
Solution Approach 2:
The patent uses a simpler, computationally cheaper calculation method that sacrifices some theoretical precision for significant reductions in power consumption and device complexity. The simplified algorithm is sufficient for the practical application of motion tracking.
2Measurement precision
If complex convergence algorithms are used to calculate attitude matrix, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent removes the complex convergence algorithms and multi-sensor processing requirements, retaining only the essential gyroscope-based calculation. This dramatically simplifies the device complexity while maintaining the core functionality of attitude matrix calculation.
3Measurement precision
If complex matrix inversion and covariance matrix calculation are performed during convergence, then measurement precision is improved, but loss of substance (memory usage) increases
Solution Approach 1:
The patent extracts and eliminates the matrix inversion and covariance matrix calculation steps from the algorithm. By using only gyroscope data with a simplified calculation approach, these computationally intensive operations are removed, significantly reducing memory requirements.
Data Source
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AI summary
An attitude matrix calculation method and a device are disclosed and relate to the field of terminal technologies, to reduce a calculation amount, reduce power consumption of a device, and save memory of the device. The method is as follows: calculating, based on a first coordinate transformation matrix and a measured value of a gyroscope that is corresponding to the ith sampling moment, a second coordinate transformation matrix corresponding to the ith sampling moment, where the first coordinate transformation matrix is a constant matrix, the first coordinate transformation matrix is a coordinate transformation matrix between a sensor coordinate system and a foot coordinate system, the second coordinate transformation matrix is a coordinate transformation matrix between the foot coordinate system and a ground coordinate system, and i is an integer greater than 0; and calculating, based on the first coordinate transformation matrix and the second coordinate transformation matrix corresponding to the ith sampling moment, an attitude matrix corresponding to the ith sampling moment, where the attitude matrix is a coordinate transformation matrix between the sensor coordinate system and the ground coordinate system.