Multimodal Signal Analysis for Imprecise Gaze and Gesture Commands
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Solution Overview
Problem
Current systems for detecting and responding to natural human movements and conversational queries are limited by their reliance on specific domains and locations, and often require precise cues, failing to effectively utilize naturalistic human behaviors for general purposes.
Innovation Solution
A method and system that utilize multimodal signal analysis to process commands and queries by combining gaze and gesture data, even with imprecise cues, to identify and act upon entities of interest, using a variety of sensors and databases to provide accurate and timely responses without requiring constrained interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If systems use specific domain restrictions and precise cue requirements, then measurement precision is improved, but adaptability deteriorates
Solution Approach 1:
The system employs multiple signal modalities (gaze tracking, gesture recognition, speech processing) that can function across different domains and contexts. Each modality serves multiple purposes: gaze data can identify objects of interest, determine user attention, or indicate reading direction; gesture data can complement speech commands or independently control system functions. This multi-functional approach allows the system to adapt to various domains while maintaining acceptable precision through redundant measurement channels.
Solution Approach 2:
The system merges data from multiple independent signal modalities to compensate for the imprecision of individual cues. By combining gaze direction data with gesture data and speech recognition, the system creates a more robust and adaptable command interpretation framework. The fusion of these modalities allows the system to maintain measurement precision across diverse domains by cross-validating signals from different sources.
2Device complexity
If systems require constrained interfaces and specific camera angles, then device complexity is reduced, but ease of operation deteriorates
Solution Approach 1:
The system automatically processes natural human behaviors (gaze movements, spontaneous gestures, conversational speech) without requiring users to learn specialized interface protocols. The multimodal signal processing framework self-adapts to capture and interpret these natural cues, eliminating the need for constrained interfaces while maintaining manageable complexity through automated signal fusion and interpretation algorithms.
3Adaptability or versatility
If systems use multiple signal modalities, then adaptability is improved, but device complexity deteriorates
Solution Approach 1:
The system segments the complex task of natural language command interpretation into distinct processing stages: gaze signal acquisition and processing, gesture signal acquisition and processing, speech recognition, temporal correlation analysis, and final command synthesis. Each module handles a specific aspect of signal processing independently, then integrates results through temporal correlation. This segmentation manages complexity by creating modular, independently testable components while maintaining high adaptability through the comprehensive multimodal approach.
Data Source
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AI summary
A first set of signals corresponding to a first signal modality (such as the direction of a gaze) during a time interval is collected from an individual. A second set of signals corresponding to a different signal modality (such as hand-pointing gestures made by the individual) is also collected. In response to a command, where the command does not identify a particular object to which the command is directed, the first and second set of signals is used to identify candidate objects of interest, and an operation associated with a selected object from the candidates is performed.