AR Movement Tracking via Depth Landmark Adjustment
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Solution Overview
Problem
Conventional motion analysis for fitness and health requires significant computational effort, is time-consuming, and often necessitates cloud computing, while also being limited by light conditions and the need for ML model training, making real-time personalized tracking challenging.
Innovation Solution
A computer-implemented method for movement tracking that uses depth image data to adjust anatomical landmark locations relative to a predefined point, enabling precise tracking in both real and virtual environments, and allowing for real-time analysis without ML model training, using LIDAR sensors for improved performance under varying conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional camera systems are used for motion tracking, then tracking can be performed, but significant computational effort and time are required, often necessitating cloud computing services
Solution Approach 1:
The patent replaces complex mechanical/computational image processing systems with a simpler depth-based tracking approach. By using depth image data directly from sensors like Kinect, the system eliminates the need for complex ML model training and conventional camera processing, achieving real-time tracking with reduced computational requirements on mobile devices
Solution Approach 2:
The patent extracts only the essential depth information from the full image data stream. By focusing solely on depth image data rather than processing complete color images through complex algorithms, the system reduces computational burden while maintaining tracking accuracy and enabling real-time operation
2Measurement precision
If ML model training is used for personalized tracking, then tracking accuracy improves, but many repetitions of movements are required which increases time consumption
Solution Approach 1:
The patent performs preliminary depth data collection during a predefined time period before actual tracking begins. This preliminary phase captures depth information that is then used to establish the baseline for movement analysis, eliminating the need for repeated ML training while maintaining personalized tracking accuracy
Solution Approach 2:
The patent creates a simplified copy of the tracking system that operates independently of complex ML models. By using depth image data directly to track anatomical landmarks and calculate movements, the system achieves personalized results without requiring time-consuming model training repetitions
3Adaptability or versatility
If conventional camera systems are used, then tracking can be performed, but good light conditions and sufficient contrast are required which limits adaptability
Solution Approach 1:
The patent substitutes conventional optical camera systems with depth sensing technology that operates independently of visible light conditions. By using infrared or active depth sensors, the system can function accurately in poor light conditions where conventional cameras fail, significantly improving adaptability to varying environmental conditions
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
Enables precise and real-time movement tracking in augmented reality environments, reducing computational requirements and eliminating the need for ML model training, while providing robustness against camera movement and improving tracking accuracy under poor light conditions.
Implementation Method 1
obtaining depth image data of a subject's body
Implementation Method 2
using LIDAR sensors for improved performance under varying conditions
Data Source
AI summary
Systems and computer-implemented methods for tracking movement of a subject comprise and perform the steps of: Obtaining depth image data of a subject's body during a predefined time period and tracking locations of a plurality of anatomical landmarks of the body based on the depth image data. In addition, a location of a predefined point is tracked in an environment, in which the subject is moving. The location of the one or more of the plurality of anatomical landmarks is adjusted relative to the location of the predefined point to obtain adjusted locations. A movement of the subject's body is determined based on the adjusted locations during the predefined time period.