Multidimensional Gesture-Voice Interaction with Dynamic Weighting
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Solution Overview
Problem
Traditional interaction methods for smart devices are limited to single input modes like touch or voice, which can be non-intuitive and inefficient in certain scenarios, necessitating a richer and more natural interactive experience.
Innovation Solution
A multidimensional perceptual interaction method and system that integrates gesture and voice data, utilizing a deconvolutional neural network for feature extraction, unscented Kalman filter for weight optimization, and support vector machine for decision-making, to provide dynamic and personalized feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional single input mode interaction methods are used, then device complexity is reduced, but interaction efficiency and intuitiveness deteriorate
Solution Approach 1:
The patent combines multiple interaction modalities (gesture recognition, voice recognition, touch input) into a unified interaction system. The interaction decision-making model integrates features from different modalities and uses dynamic weight adjustment to combine them effectively, resolving the contradiction by merging separate interaction systems into a coordinated multi-modal system that improves efficiency without proportionally increasing complexity
Solution Approach 2:
The interaction system is designed to handle multiple types of inputs (gestures, voice, touch) through a universal decision-making framework. The same neural network architecture and weight adjustment mechanism process different interaction modalities, allowing the system to maintain relatively low complexity while providing diverse and efficient interaction methods across different scenarios
2Adaptability or versatility
If static weight assignment for interaction instructions is used, then calculation complexity is reduced, but adaptability to user habits deteriorates
Solution Approach 1:
The patent implements dynamic weight adjustment where the weight of each interaction instruction is not fixed but adapts based on historical usage data. The unscented Kalman filter continuously optimizes weights in real-time based on user behavior patterns, transforming a static weight system into a dynamic one that adapts to user habits while managing complexity through efficient filtering algorithms
Solution Approach 2:
The interaction weight model automatically learns and adjusts weights based on user interaction history without requiring manual configuration. The system serves itself by using its own historical data to optimize its decision-making parameters, reducing the need for external tuning while improving adaptability to individual user preferences
3Measurement precision
If deconvolutional neural network is used for feature extraction, then feature extraction precision is improved, but computational energy consumption increases
Solution Approach 1:
The feature extraction process is divided into distinct stages: gesture data processing through convolutional layers, voice data processing through separate feature extraction, and integration through the deconvolutional layer. This segmentation allows the system to apply computationally intensive operations only where necessary while using lighter processing for other modalities, reducing overall energy consumption while maintaining precision
Solution Approach 2:
The patent performs preliminary feature extraction and normalization on raw gesture and voice data before feeding them into the main decision-making model. By pre-processing data to extract relevant features in advance, the system reduces the computational burden during real-time interaction decisions, lowering energy consumption while preserving extraction precision
Data Source
AI summary
A multidimensional perceptual interaction method and system are provided. The multidimensional perceptual interaction method includes: collecting historical data of a user, where the historical data includes first interaction data, second interaction data, a corresponding interaction instruction of the first interaction data, and a corresponding interaction instruction of the second interaction data; extracting an interaction feature from the historical data, and training an interaction feature model; establishing a dynamic weight for the interaction instruction in the historical data, and establishing an interaction weight model; establishing an interaction decision-making model based on the interaction feature, the dynamic weight, and the interaction instruction; and inputting to-be-detected interaction data into the interaction feature model to extract a feature, inputting the extracted feature and the dynamic weight into the interaction decision-making model to obtain an interaction instruction, and executing the interaction instruction to provide a multidimensional feedback.