Finger Encoding Pose Classification for Dynamic Hand Gestures

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Solution Overview

Problem

Current state-of-the-art hand tracking systems struggle with effective classification of inter-gestures and dynamic hand gestures, limiting the creation of flexible gestures and dynamic motion gestures, and often face ambiguity issues between similar hand gestures.

Innovation Solution

A finger encoding based pose classification model that captures iterative transition dynamics between different finger states, using 3D keypoints and encoding mechanisms to create flexible hand gestures and classify both traditional and inter-gestures, including dynamic hand motions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If heuristics or machine learning models are employed to classify traditional hand gestures, then classification accuracy for traditional gestures is improved, but the system fails to effectively classify inter-gestures and dynamic hand gestures

Engineering Contradiction:

Improvegesture classification accuracyVSAvoidgesture type coverage

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The hand is segmented into five individual fingers, each independently encoded with a code representing its position state. This segmentation allows the system to capture fine-grained finger configurations and transitions, enabling effective classification of both traditional gestures and inter-gestures that were previously indistinguishable.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system encodes iterative transition dynamics between different finger states rather than static positions alone. By capturing the dynamic transitions of fingers between states, the system can distinguish between similar gestures and effectively classify dynamic hand motions that static analysis would fail to differentiate.

Inventive Principle:

Principle #15Dynamics

2Device complexity

If traditional pose classification algorithms are used, then processing simplicity is maintained, but the system faces ambiguity issues between similar hand gestures

Engineering Contradiction:

Improveclassification algorithm complexityVSAvoidgesture distinction accuracy

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

A finger encoding mechanism serves as an intermediary between raw hand images and final gesture classification. Each finger is assigned a code representing its position, creating a structured intermediate representation that disambiguates similar gestures while maintaining computational efficiency. This encoding layer translates complex visual information into a simplified yet discriminative format.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If the system attempts to classify all types of hand gestures including inter-gestures, then gesture recognition versatility is improved, but processing complexity increases

Engineering Contradiction:

Improvegesture classification capabilityVSAvoidprocessing system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The finger encoding mechanism provides a universal framework that handles multiple gesture types through a single unified approach. The same encoding and classification process effectively categorizes traditional gestures, inter-gestures, and dynamic motions, eliminating the need for separate specialized algorithms for different gesture categories.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250308288A1Finger encoding based pose classification

Publication Date: 2025.10.02 QUALCOMM INC

AI summary

Systems and techniques are described for image processing. For example, a computing device can encode one or more fingers of five fingers of a hand with a code, wherein the code corresponds to a position associated with the one or more fingers making the hand gesture. The computing device can determine a classification for the hand gesture, wherein the classification comprises the code associated with the one or more fingers of the hand.