Gesture Recognition Using Element Segmentation
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Solution Overview
Problem
Existing gesture recognition systems face challenges in achieving high accuracy due to the complexity of processing raw data from motion capturing sensors, often relying on simple machine learning techniques that struggle with recognizing nuanced gestures.
Innovation Solution
The system categorizes raw data into gesture elements and utilizes contextual dependency between these elements to enhance recognition accuracy, allowing for a more efficient and accurate gesture recognition process by breaking down gestures into predefined movements and analyzing their temporal relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple machine learning techniques are used on raw data, then device complexity is reduced, but gesture recognition accuracy deteriorates
Solution Approach 1:
The patent segments raw gesture data into discrete gesture elements with specific characteristics (direction, magnitude, duration). This segmentation allows the system to process complex gestures as sequences of simpler, categorized elements rather than attempting to analyze entire raw data sequences, thereby reducing processing complexity while improving recognition accuracy through element-level classification.
Solution Approach 2:
The patent performs preliminary categorization of raw data into gesture elements before final gesture recognition. By pre-processing the data to identify and classify basic movement elements (such as directional movements, circular motions, or pauses) and their contextual relationships, the system simplifies the subsequent recognition task while maintaining high accuracy through the structured element framework.
2Measurement precision
If contextual dependency between gesture elements is utilized, then gesture recognition accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the contextual dependency analysis into element-level relationships rather than analyzing entire gesture sequences globally. By examining contextual relationships between adjacent gesture elements (such as the relationship between a circular motion element followed by a directional element), the system achieves accurate gesture recognition through localized contextual analysis, reducing overall processing complexity.
Solution Approach 2:
The patent applies different processing strategies to different gesture elements based on their local characteristics. Each gesture element is analyzed with consideration of its specific properties (direction, magnitude, duration) and its contextual relationship with neighboring elements, allowing the system to achieve high recognition accuracy through differentiated local analysis rather than uniform global processing.
Data Source
AI summary
Aspects of the present disclosure provide a gesture recognition method and an apparatus for capturing gesture. The apparatus categorizes the raw data of a gesture into gesture elements, and utilizes the contextual dependency between the gesture elements to perform gesture recognition with a high degree of accuracy and small data size. A gesture may be formed by a sequence of one or more gesture elements.