Personalized Gesture Recognition Using Speech-Gesture Associations
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Solution Overview
Problem
Existing gesture recognition systems struggle to accurately identify user intents from personal and non-standard gestures due to limitations in general gesture-classification models, which fail to account for individual user-specific movements.
Innovation Solution
A personalized gesture-classification model is trained using both speech and gesture inputs, incorporating gesture components and temporal information to enhance recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a general gesture-classification model is used, then the system can handle diverse gestures from multiple users, but it fails to accurately recognize individual user-specific and nonstandard gestures
Solution Approach 1:
The system segments gesture recognition into two distinct models: a general gesture-classification model for handling diverse gestures from multiple users, and a personalized gesture-classification model for capturing individual user-specific patterns. This segmentation allows each model to specialize in its respective domain, resolving the contradiction between handling diversity and achieving personalization accuracy.
Solution Approach 2:
The system dynamically adapts by training the personalized gesture-classification model using speech-gesture associations specific to each user. This dynamic personalization enables the system to evolve and adapt to individual user behaviors over time, maintaining high accuracy for user-specific gestures while preserving the general model's broad coverage.
2Ease of operation
If traditional text and voice inputs are used, then the interaction method is simple and reliable, but the user interaction experience is limited and less engaging
Solution Approach 1:
The system implements multi-functional input handling by supporting multiple interaction modalities: text input, voice input, and gesture input. Each modality serves the same core function of user communication but offers different interaction experiences. The personalized gesture recognition extends the system's versatility while maintaining the simplicity and reliability of traditional inputs through the same unified processing framework.
3Measurement precision
If a personalized gesture-classification model is trained for each user, then user-specific gesture recognition accuracy improves, but the system complexity and training data requirements increase
Solution Approach 1:
The system performs preliminary action by collecting speech-gesture association data during normal user interactions with the assistant. Instead of requiring separate, complex training sessions, the system continuously gathers training data from everyday use, preparing the personalized model incrementally. This preliminary data collection approach simplifies the overall process by integrating model training into regular system operation.
Solution Approach 2:
The personalized gesture-classification model serves itself by automatically training on the user's own speech-gesture associations without requiring external intervention or manual configuration. The system self-adapts to each user's unique gesture patterns by processing their interaction data, reducing the complexity burden on users while achieving high personalization accuracy.
Data Source
AI summary
In one embodiment, a method includes receiving a user request from a first user from a client system associated with a first user, wherein the user request comprise a gesture-input from the first user and a speech-input from the first user, determining an intent corresponding to the user request based on the gesture-input by a personalized gesture-classification model associated with the first user, executing one or more tasks based on the determined intent and the speech-input, and sending instructions for presenting execution results of the one or more tasks to the client system responsive the user request.