Hand Tracking via Digit Center-Line Approximation
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Solution Overview
Problem
Existing hand-tracking systems in interactive spaces face challenges in accurately detecting hands due to interference from wearable items and the absence of arm features, leading to incomplete or inaccurate hand detection.
Innovation Solution
The system employs depth sensors to generate position information of hand surfaces, identifies candidate points for hand detection, and analyzes these points without relying on arm presence, using machine-readable instructions to process and determine finger and metacarpophalangeal joints, thereby identifying and tracking hands in interactive spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system relies on arm surface points for hand detection, then detection accuracy is improved, but the system becomes vulnerable to interference from wearable items and cannot function when arm features are obscured
Solution Approach 1:
The patent extracts the hand detection task from the arm context by identifying hand surfaces independently through point cloud analysis. The system detects hand surfaces by analyzing geometric characteristics and spatial relationships of points without requiring arm surface points, thereby removing the harmful dependency on arm features that can be obscured by wearables.
Solution Approach 2:
The patent segments the hand into distinct surface regions (dorsal hand surface, palmar hand surface, digit surfaces) and analyzes each segment independently. This segmentation allows the system to identify hand features without relying on continuous arm surface data, enabling accurate hand detection even when arm features are blocked by wearable items.
2Extent of automation
If the system uses depth sensors to detect hand surfaces, then hand tracking capability is enabled, but detection reliability decreases when wearable items obscure arm features
Solution Approach 1:
The system performs self-service by automatically identifying hand surfaces through point cloud analysis without requiring external reference frames or arm feature validation. The hand detection algorithm independently validates each candidate surface based on geometric criteria and spatial relationships, ensuring reliable detection even when arm features are obscured by wearables.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously validates detected hand surfaces against anatomical constraints and updates the hand model accordingly. This feedback loop allows the system to maintain detection reliability by correcting misidentifications and adapting to occlusions caused by wearable items.
3Measurement precision
If the system identifies individual digits and joints, then gesture recognition accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the hand surface into digit regions and identifies joints (metacarpophalangeal joints, fingertips) as distinct features. This segmentation enables precise gesture recognition by analyzing the relative positions and orientations of individual digits and joints, achieving high accuracy without excessive processing complexity through efficient point cloud processing algorithms.
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
This approach enables accurate hand detection and tracking in interactive spaces, including augmented and virtual reality environments, by effectively distinguishing hand features and gestures, even when arm features are obscured or absent.
Implementation Method 1
a distancing device (e.g., a depth sensor) may be configured to generate output signals conveying position information of the hand
Data Source
AI summary
A system configured for tracking a human hand, e.g., in an interactive space, may comprise a distancing device, one or more physical processors, and/or other components. The distancing device may be configured to generate output signals conveying position information. The position information may include positions of surfaces of real-world objects, including surfaces of a human hand. A group of points may be identified that lie on a candidate surface which is a candidate for being the surface of a hand. The edges of the candidate surface may be detected. The points furthest away from the closest edge may be determined. A model may be created by connecting at least some of these points. Sections of the model are identified that may be a digit of the hand. Per section, fingertips and metacarpophalangeal joints may be identified. The model may be analyzed to determine whether the candidate surface is the surface of a hand.