Robot Emotional State Recognition via Sensor Fusion
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Solution Overview
Problem
Conventional robot interactions lack emotional engagement and context detection, resulting in inadequate responses to user inputs, as they fail to recognize the emotional state and intent behind gestural and voice inputs.
Innovation Solution
A method and apparatus for a robot device that determines the emotional state of a user by mapping gestural and voice inputs to a set of emotions, using sensors to detect parameters like pressure, heart rate, and gesture patterns, and dynamically interacts with the user based on this emotional state, employing an emotion model to provide enhanced responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robot uses conventional interaction methods (voice input transformation to actionable intents), then the interaction is simple and direct, but the interaction lacks emotional engagement and context detection
Solution Approach 1:
The interaction system is segmented into multiple independent modules: voice input processing module, gestural input processing module, emotional state determination module, and dynamic interaction module. Each module handles specific functions, allowing the system to detect emotions and context without overwhelming complexity. The segmentation enables parallel processing of different input types and emotional parameters.
Solution Approach 2:
The robot device is designed with multi-functionality to handle both conventional voice inputs and emotional context detection through a unified interaction framework. The same device structure processes simple commands and emotional gestures simultaneously, making the system adaptable to various interaction scenarios without requiring separate dedicated systems for each function.
2Measurement precision
If the robot maps inputs to emotions and dynamically interacts based on emotional state, then the user experience is enhanced with personalized responses, but the processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-defining emotion models and mapping relationships between input patterns and emotional states before actual interaction occurs. Gesture patterns, pressure thresholds, and voice tone characteristics are pre-configured with corresponding emotional interpretations, enabling rapid emotional state determination during runtime without complex real-time computation.
Solution Approach 2:
The dynamic interaction module implements feedback mechanisms where the robot's responses are adjusted based on detected emotional states, and the system learns from user reactions to refine emotional recognition accuracy. The mapping between inputs and emotions is continuously optimized based on interaction outcomes, improving measurement precision while managing complexity through adaptive learning.
3Measurement precision
If the robot detects multiple parameters (pressure, heart rate, gesture speed, gesture pattern), then the emotional state determination becomes more accurate, but the sensor requirements and system complexity increase
Solution Approach 1:
Multiple sensing functions are merged into integrated sensor assemblies. For example, pressure sensors and gesture detection sensors are combined in the same physical components, and voice input processing is integrated with emotional tone analysis. This merging reduces the total number of separate sensors required while maintaining the ability to detect multiple parameters simultaneously for accurate emotional state determination.
Data Source
AI summary
Disclosed is a method for a social interaction by a robot device. The method includes receiving an input from a user, determining an emotional state of the user by mapping the received input with a set of emotions and dynamically interacting with the user based on the determined emotional state in response to the input. Dynamically interacting with the user includes generating contextual parameters based on the determined emotional state. The method includes determining an action in response to the received input based on the generated contextual parameters and performing the determined action. The method further includes receiving another input from the user in response to the performed action and dynamically updating the mapping of the received input with the set of emotions based on the other input for interacting with the user.