Nine-Axis Motion Sensor for Accurate 3D Deviation Mapping
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Solution Overview
Problem
Existing electronic devices with 5-axis motion sensors struggle to accurately detect and compensate for movements and rotations in dynamic environments, particularly due to limitations in detecting 3D deviation angles and handling external interferences, leading to inaccurate mapping of pointer movements on a 2D display.
Innovation Solution
A nine-axis motion sensor module comprising accelerometers, magnetometers, and gyroscopes is used to detect axial accelerations, magnetism, and angular velocities, with an enhanced comparison method to eliminate errors and noises, allowing for accurate calculation and output of yaw, pitch, and roll angles in a 3D spatial reference frame, and their mapping onto a 2D display frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 5-axis motion sensor is used, then the device can detect movements and rotations, but the measurement precision of 3D deviation angles is insufficient and external interferences cannot be properly handled
Solution Approach 1:
The motion detection system is segmented into three independent sensor modules: accelerometer for linear acceleration, gyroscope for angular velocity, and magnetometer for magnetic field measurement. Each sensor handles specific aspects of motion detection, allowing the system to achieve comprehensive 3D deviation angle measurement by combining results from these segmented functional components.
Solution Approach 2:
The patent merges the outputs of multiple sensor types (accelerometer, gyroscope, and magnetometer) into a unified motion detection system. By combining the strength of each sensor type - linear acceleration data from the accelerometer, angular velocity from the gyroscope, and magnetic orientation from the magnetometer - the system achieves superior 3D deviation angle measurement accuracy that cannot be obtained by any single sensor alone.
2Reliability
If motion sensors are used in dynamic environments, then movement detection is enabled, but external interferences cause inaccurate pointer mapping on display
Solution Approach 1:
The system converts the harmful effect of external magnetic field interferences into a beneficial feature by introducing the magnetometer specifically to detect and compensate for these interferences. The magnetometer measures the ambient magnetic field and provides correction data that compensates for distortions caused by external magnetic sources, thereby improving the reliability of orientation detection in dynamic environments.
Solution Approach 2:
The system implements feedback mechanisms where sensor data is continuously processed and corrected based on real-time measurements. The magnetometer provides feedback about magnetic field conditions, and this information is used to adjust and correct the orientation calculations from the accelerometer and gyroscope, creating a closed-loop system that compensates for external interferences.
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
The solution enables accurate and absolute detection of device movements and rotations in dynamic environments, reducing the impact of external interferences and ensuring precise mapping of pointer movements on a 2D display, even in conditions with continuous nonlinear movements and rotations.
Implementation Method 1
an accelerometer to measure or detect axial accelerations Ax, Ay, Az
Implementation Method 2
a magnetometer to measure or detect magnetism Mx, My, Mz
Implementation Method 3
a rotation sensor to measure or detect angular velocities ωx, ωy, ωz
Data Source
AI summary
A representative method involves: generating measured angular velocities and measured axial accelerations; calculating a resulting deviation associated with movements and rotations in a spatial reference frame by: providing a previous quaternion corresponding to time T−1 based on the measured axial accelerations corresponding to time T−1 and the measured angular velocities corresponding to time T−1; converting the measured angular velocities corresponding to time T based on the previous quaternion into a current quaternion and predicted axial accelerations; comparing the predicted axial accelerations with the measured axial accelerations corresponding to time T to obtain a first comparison result; obtaining an updated quaternion associated with time T based on the current quaternion and the first comparison result, and using the updated quaternion as a next occurrence of the previous quaternion; and providing the resulting deviation based on the updated quaternion; and, providing content based on the resulting deviation in the spatial reference frame.