Hybrid Pose Calculation with Region-Based Static and Motion Modes
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Solution Overview
Problem
Existing pose calculation technologies face challenges with low detection frequency and accuracy issues due to environmental factors and drift errors in real-time image analysis and inertial sensors, respectively.
Innovation Solution
A pose calculating apparatus and method that adjusts the pose calculating mode for each body region using a combination of real-time images and inertial measurement parameters, switching between static and motion modes to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time image analysis is used for pose calculation, then the system can provide visual feedback, but the low frame rate (30 fps) causes inaccurate pose detection during exercise
Solution Approach 1:
The patent combines real-time image analysis (30 fps) with inertial sensor data (60 fps) to create a hybrid pose calculation system. The inertial sensor provides high-frequency motion data that compensates for the low frame rate of image analysis, while the image data provides contextual information to reduce drift accumulation. This merging of two different measurement systems resolves the contradiction between frame rate and pose detection accuracy.
2Speed
If inertial sensor is used for pose calculation, then the detection frequency is high (60 fps), but drift values accumulate over time causing accuracy to decrease
Solution Approach 1:
The system uses real-time image analysis results as feedback to correct and reset drift values from inertial sensors. The image-based pose calculation, while lower frequency, provides periodic reference points that reset the accumulating drift in the inertial sensor data. This feedback mechanism maintains high detection frequency while preventing long-term accuracy degradation.
3Adaptability or versatility
If computer vision is used for pose calculation, then the system can process visual data, but environmental problems (occlusion, insufficient light, low contrast) cause calculation errors
Solution Approach 1:
The inertial sensor acts as an intermediary that provides reliable pose data when visual conditions are poor. When environmental factors (occlusion, lighting, contrast) degrade image quality, the system automatically relies more on inertial sensor data which is unaffected by these conditions. This intermediary approach ensures continuous accurate pose tracking regardless of environmental challenges.
4Speed
If inertial sensor is used for pose calculation, then the detection frequency is high, but the resetting process is cumbersome and user movement causes sensor position changes reducing accuracy
Solution Approach 1:
The system automatically performs drift correction using image-based pose references without requiring manual user intervention for resetting. The processor continuously compares inertial sensor data with image analysis results and automatically adjusts for drift accumulation. This self-service approach eliminates the need for users to manually reset sensors while maintaining high detection frequency and accuracy.
Data Source
AI summary
A pose calculating apparatus and method are provided. The pose calculating apparatus receives a plurality of real-time images and a plurality of inertial measurement parameters corresponding to at least one inertial sensor worn by a user. The pose calculating apparatus determines a pose calculating mode corresponding to each of a plurality of body regions of the user based on the real-time images and the inertial measurement parameters, wherein the pose calculating mode corresponds to a static mode or a motion mode. The pose calculating apparatus calculates a pose corresponding to each of the body regions based on the pose calculating mode corresponding to each of the body regions.