xR Gesture Recognition via Hand Count Segmentation
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Solution Overview
Problem
Current virtual, augmented, and mixed reality applications lack the ability to effectively distinguish between one-handed and two-handed gesture sequences, which is crucial for accurate user input and interaction in immersive environments.
Innovation Solution
An Information Handling System (IHS) equipped with a processor and memory, utilizing an Automatic Light Sensor (ALS) and camera to detect and identify gesture sequences by extracting features from video frames, and performing table look-up operations using calibration data to differentiate between one-handed and two-handed gestures based on motion velocity and asynchronicity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If gesture recognition systems process all gestures uniformly without distinguishing hand usage, then system complexity is reduced, but gesture recognition accuracy deteriorates
Solution Approach 1:
The gesture recognition system is segmented into specialized components: one-handed gesture recognition module and two-handed gesture recognition module. Each module is optimized for specific gesture types, allowing accurate differentiation between single-hand and dual-hand interactions without requiring the entire system to handle all gesture variations uniformly.
Solution Approach 2:
The system performs preliminary classification to determine whether a gesture sequence involves one hand or two hands before proceeding with detailed recognition. This preliminary differentiation enables the system to apply appropriate recognition algorithms tailored to each gesture type, improving overall accuracy while maintaining manageable complexity through staged processing.
2Measurement precision
If the system uses multiple sensors and processing steps to distinguish gesture sequences, then gesture recognition accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary analysis using available sensor data to quickly determine the number of hands involved in a gesture sequence. This early classification enables subsequent processing to be optimized based on the gesture type, reducing overall processing time while maintaining accurate differentiation between one-handed and two-handed gestures.
Solution Approach 2:
The gesture recognition system utilizes data already being collected by sensors mounted on the HMD for other purposes (such as tracking and environmental sensing). By repurposing existing sensor inputs for gesture differentiation, the system avoids additional measurement steps and reduces processing overhead while maintaining recognition accuracy.
Data Source
AI summary
Systems and methods for distinguishing between one-handed and two-handed gesture sequences in virtual, augmented, and mixed reality (xR) applications are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive a gesture sequence from a user wearing a Head-Mounted Device (HMD) coupled to the IHS, where the HMD is configured to display an xR application, and identify the gesture sequence as: (i) a one-handed gesture sequence, or (ii) a two-handed gesture sequence.