Adaptive Multimodal Gesture Recognition for AR/VR
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Solution Overview
Problem
Existing multimodal hand gesture recognition systems face challenges in power efficiency and resource management, particularly in resource-limited environments like AR/VR glasses, due to their static design and high complexity.
Innovation Solution
An adaptive multimodal hand gesture recognition system that dynamically adjusts the significance and use of different modalities based on the specific gesture class being recognized, employing adaptive sensing and progressive multi-step adaptation techniques, as well as channel attention and channel swapping methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If synchronized streams of RGB, depth, and flow images are used for gesture recognition, then recognition accuracy is improved, but power consumption and computational resource requirements increase
Solution Approach 1:
The system dynamically adjusts the sensing configuration by switching between single-modality and multi-modality operation based on gesture complexity. For simple gestures, only one modality (e.g., RGB) is activated, while for complex gestures, additional modalities (depth, flow) are activated to improve recognition accuracy only when necessary, thereby reducing overall power consumption.
Solution Approach 2:
The system changes the operational parameters of the sensing system by adjusting the frame rates of different modalities and selectively activating or deactivating specific sensors based on the detected gesture type. This parameter adaptation allows the system to optimize the balance between recognition accuracy and power consumption for different gesture scenarios.
2Measurement precision
If synchronized streams of RGB, depth, and flow images are used for gesture recognition, then recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The system employs dynamic modality selection where the complexity of the sensing system is adjusted in real-time based on gesture requirements. A gesture classifier analyzes incoming data and dynamically determines which modalities are needed, allowing the system to operate in a simplified single-modality mode for simple gestures and switch to complex multi-modality mode only when necessary for accurate recognition.
Solution Approach 2:
The gesture recognition system is segmented into independent processing streams for different modalities (RGB, depth, flow), each with its own neural network classifier. This segmentation allows the system to selectively activate only the necessary processing streams based on gesture complexity, reducing the overall computational burden and system complexity while maintaining high accuracy for complex gestures.
3Device complexity
If static sensing system design is used, then system simplicity is maintained, but adaptability to varying gesture complexities is reduced
Solution Approach 1:
The system incorporates a feedback mechanism where a gesture classifier continuously analyzes the input data and provides feedback on gesture complexity. Based on this feedback, the system dynamically adjusts the sensing configuration by activating or deactivating specific modalities and adjusting frame rates, enabling the system to adapt to varying gesture complexities while maintaining a relatively simple base architecture.
Solution Approach 2:
The sensing system performs self-adjustment by automatically determining the appropriate modality configuration based on the detected gesture type. The system serves itself by making real-time decisions about resource allocation and sensing intensity without requiring external intervention, thereby achieving adaptability while maintaining design simplicity through automated control.
Data Source
AI summary
A system and a method for performing gesture recognition are disclosed, the method comprising detecting a gesture using a primary modality; evaluating an expected accuracy gain (EAG) to identify a modality that yields a maximum relative EAG among the primary modality and one or more secondary modalities; and activating the one or more secondary modalities for detecting the gesture if the one or more secondary modalities correspond to the modality that yields the maximum relative EAG.