Dual Neural Network Gesture Recognition for AR
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Solution Overview
Problem
Existing gesture recognition systems in augmented reality devices face challenges in distinguishing between user interactions with virtual content and everyday activities, leading to false positives and a tradeoff between subtle gestures and strict recognition criteria.
Innovation Solution
A method using two neural networks to evaluate gesture input, where the first neural network assesses the likelihood of subsequent gesture interactions based on a sequence of data frames, and the second neural network adjusts recognition parameters and evaluates the user's current gesture interactions during a predetermined window.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If strict recognition criteria are used to distinguish user interactions from everyday activities, then false positives are reduced, but gesture recognition accuracy deteriorates due to inability to recognize subtle gestures
Solution Approach 1:
The system divides gesture recognition into two separate neural networks: the first network evaluates likelihood of gesture interactions, and the second network performs actual gesture recognition. This segmentation allows each network to specialize - the first in distinguishing intent and the second in precise gesture classification - thereby resolving the contradiction between reducing false positives and maintaining recognition accuracy.
Solution Approach 2:
The first neural network performs preliminary evaluation of gesture likelihood before the second network conducts detailed gesture recognition. By pre-assessing whether a gesture interaction is likely based on sequential data frames, the system prepares the recognition parameters in advance, allowing the second network to focus on accurate gesture identification only when necessary, thus reducing false positives without sacrificing accuracy.
2Ease of operation
If relaxed gesture requirements are used to enable subtle gestures, then ease of operation improves, but false positives increase due to difficulty distinguishing from everyday activities
Solution Approach 1:
The system dynamically adjusts recognition parameters based on the output of the first neural network. When the first network detects high likelihood of gesture interaction, the second network uses more relaxed parameters to capture subtle gestures. When likelihood is low, stricter parameters are applied to filter out everyday activities. This dynamic adaptation resolves the contradiction between ease of operation and reliability.
Solution Approach 2:
The system changes recognition parameters based on contextual evaluation. The first neural network analyzes sequential data frames to determine gesture likelihood, and based on this evaluation, the second network adjusts its recognition parameters accordingly. This parameter adjustment mechanism allows the system to be sensitive to subtle gestures when appropriate while maintaining strict criteria when needed to avoid false positives.
3Measurement precision
If two neural networks are used to evaluate gesture input, then gesture recognition accuracy improves, but device complexity increases
Solution Approach 1:
The complex task of gesture recognition is segmented into two specialized neural networks rather than one monolithic system. The first network handles temporal sequence analysis and likelihood evaluation, while the second network focuses on gesture pattern recognition. This segmentation improves accuracy by dividing computational tasks according to their specific requirements, while the modular structure makes the overall system more manageable despite the increased number of components.
Solution Approach 2:
The two neural networks work together as an integrated multi-functional system. The first network provides contextual evaluation that informs the second network's recognition process, creating a universal framework that handles both subtle gestures and everyday activity differentiation. This multi-functional approach allows the system to achieve high accuracy across diverse gesture types without requiring separate specialized systems for each gesture category.
Data Source
AI summary
A method for evaluating gesture input comprises receiving input data for sequential data frames, including hand tracking data for hands of a user. A first neural network is trained to recognize features indicative of subsequent gesture interactions and configured to evaluate input data for a sequence of data frames and to output an indication of a likelihood of the user performing gesture interactions during a predetermined window of data frames. A second neural network is trained to recognize features indicative of whether the user is currently performing one or more gesture interactions and configured to adjust parameters for gesture interaction recognition during the predetermined window based on the indicated likelihood. The second neural network evaluates the predetermined window for performed gesture interactions based on the adjusted parameters, and outputs a signal as to whether the user is performing one or more gesture interactions during the predetermined window.