Motion Mode Detection Using Spectral Distortion Analysis
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Solution Overview
Problem
Satellite-based navigation systems face limitations in areas with blocked or attenuated signals, such as deep canyons, urban environments, and indoors, where inertial measurement units (IMUs) with MEMS sensors can aid but do not accurately account for motion modes beyond velocity, acceleration, or heading changes.
Innovation Solution
A method and apparatus that collect movement data from an IMU, compare it to training data sets for different motion modes using linear predictor coefficients (LPC) and spectral distortion values, and determine the current motion mode by minimizing spectral distortion, with optional absolute difference threshold comparisons to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inertial measurement units (IMUs) with MEMS sensors are integrated to provide data for position determination, then position availability and reliability are improved in degraded signal environments, but the system cannot accurately account for motion modes beyond velocity, acceleration, or heading changes
Solution Approach 1:
The system changes the parameters used for motion detection from basic kinematic parameters (velocity, acceleration, heading) to spectral characteristics (linear predictor coefficients, spectral distortion values) that capture motion mode-specific patterns. This allows the IMU to distinguish between different motion modes (walking, running, driving, stationary) based on the spectral signature of the sensor data.
Solution Approach 2:
The patent replaces direct mechanical measurement of motion modes with a signal processing approach using linear predictor coefficients and spectral analysis. Instead of mechanically detecting motion mode through additional sensors, the system substitutes a computational method that analyzes the spectral characteristics of existing IMU data to identify motion modes.
2Reliability
If motion mode detection is added to improve navigation accuracy, then navigation reliability in degraded environments is improved, but device complexity increases
Solution Approach 1:
The system achieves multi-functionality by using the existing IMU sensor data for multiple purposes: traditional position determination and new motion mode detection. The same accelerometer and gyroscope data are processed through spectral analysis to serve dual functions, eliminating the need for separate dedicated motion detection hardware and reducing overall system complexity.
Solution Approach 2:
The motion mode detection system serves itself by using the existing IMU data to detect motion modes, which then feeds back to improve the position determination algorithms. The system uses its own sensor data to enhance its performance without requiring external assistance or additional complex subsystems.
3Device complexity
If current DR methods are used that do not account for motion modes, then device complexity is kept low, but position determination accuracy is reduced in varied motion conditions
Solution Approach 1:
The system introduces dynamics by making the position determination algorithm adaptive to the current motion mode. Instead of using a static DR method that treats all movements uniformly, the system dynamically adjusts its behavior based on detected motion modes (e.g., using different algorithms for walking versus driving), thereby improving accuracy without requiring a completely complex system.
Data Source
AI summary
A method and apparatus of detecting and using motion modes in a mobile device is described. Movement data is collected from an inertial motion unit (IMU) of the mobile device and compared to two or more sets of training data, each set of training data corresponding to a different motion mode. Then, a motion mode is determined to be the current mode of the mobile device on the results of the comparison. The motion mode is used by the mobile device in a variety of applications.