Hand Tracking Motion Curves for Object Holding Detection
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Solution Overview
Problem
Existing hand tracking technologies in extended reality (XR) systems face challenges in accurately determining when a hand is holding an object, leading to misinterpretations and disruptions due to limited camera field of view, object variations, and difficulty in distinguishing between hands, which affects the immersive experience.
Innovation Solution
A hand tracking device and method that utilizes a processor to analyze hand and object motion curves using a camera and tracker data to determine whether a hand is holding an object, employing curve fitting algorithms and sensor data to enhance accuracy and intuitiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hand tracking is performed using camera data only, then the system is simple, but tracking accuracy deteriorates when hands hold objects due to occlusion and limited field of view
Solution Approach 1:
The patent combines camera data with tracker data (from IMU sensors) to determine hand motion curves. This multi-source data fusion approach maintains tracking accuracy when hands hold objects by compensating for camera occlusion limitations through inertial measurement data.
Solution Approach 2:
The patent introduces motion curves as an intermediary representation that bridges camera observations and tracker measurements. By fitting motion curves to both data sources and comparing their consistency, the system indirectly determines whether hands are holding objects, resolving the contradiction between simple camera-based tracking and accurate occlusion handling.
2Measurement precision
If the system uses multiple data sources (camera and tracker) to improve accuracy, then tracking precision improves, but device complexity increases
Solution Approach 1:
The patent segments the hand tracking problem into distinct components: camera-based hand detection, tracker-based motion measurement, motion curve fitting for each data source, and consistency comparison. This modular segmentation manages complexity by handling each data source and processing step separately before integration.
Solution Approach 2:
The patent employs dynamic motion curve fitting that adapts to different hand states (holding vs. not holding objects). The system dynamically adjusts its analysis based on real-time data consistency between camera and tracker, allowing flexible handling of varying tracking conditions without rigid structural complexity.
3Reliability
If the camera field of view is limited, then the device design is compact, but hand detection reliability deteriorates when hands are occluded or out of view
Solution Approach 1:
The patent transitions from purely visual 2D camera detection to incorporating 3D spatial tracking data from inertial sensors. By adding the temporal and inertial measurement dimensions, the system reliably detects hands even when they move out of the limited camera field of view or become occluded, as the tracker continues providing motion data.
Data Source
AI summary
This disclosure provides a hand tracking device. The hand tracking device includes a storage circuit and a processor. The storage circuit stores a program code. The processor is coupled to the storage circuit and accesses the program code. The processor is configured to obtain a hand motion curve of a hand during a period of time. The processor is configured to obtain an object motion curve of an object during the period of time. The processor is configured to determine whether the hand is holding the object based on the hand motion curve and the object motion curve.