Multisensory Emotion Recognition System for Clinical Assessment
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Solution Overview
Problem
Current systems and methods for emotion recognition, particularly for individuals with conditions like schizophrenia, autism, and depression, face challenges in accurately perceiving and interpreting emotional states from others, as they struggle to recognize emotional cues from multisensory information.
Innovation Solution
A system and method that utilizes multisensory information, combining visual and auditory inputs, to assess and improve emotion recognition capabilities by outputting and receiving emotional state information, comparing it to reference data, and adjusting difficulty levels for iterative training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-sensory emotion recognition methods are used, then the system complexity is low, but the emotion recognition accuracy is insufficient especially for individuals with schizophrenia, autism, and depression
Solution Approach 1:
The patent combines multiple sensory inputs (visual facial expressions, auditory tone of voice, and physiological signals) into a unified emotion recognition system. The feature extraction unit processes these diverse sensory modalities simultaneously, and the emotion recognition unit integrates them to determine emotional state, thereby improving accuracy without treating each modality separately
Solution Approach 2:
The system employs a universal processing architecture where the same feature extraction and emotion recognition units handle multiple types of sensory information (visual, auditory, physiological) through unified algorithms. This multi-functional approach allows the system to process diverse data types using common computational frameworks, enhancing accuracy while managing complexity through shared processing resources
2Measurement precision
If multisensory information is processed to improve emotion recognition, then the emotion recognition capability is enhanced, but the processing time and computational resources increase
Solution Approach 1:
The patent divides the emotion recognition process into distinct sequential stages: feature extraction from multiple sensory modalities, feature fusion/integration, and emotion classification. This segmentation allows each stage to be optimized independently and enables parallel processing of different sensory features, reducing overall processing time while maintaining comprehensive multisensory analysis
Solution Approach 2:
The system performs preliminary feature extraction and preprocessing of sensory data before the main emotion recognition computation. By preparing and organizing sensory features in advance (normalizing data, extracting key characteristics), the system reduces the computational burden during the critical emotion classification phase, thereby decreasing total processing time
3Reliability
If objective assessment and iterative training are implemented, then the emotion recognition skills are improved, but the training process complexity and time requirement increase
Solution Approach 1:
The patent implements an iterative training system where the emotion recognition unit provides feedback on performance accuracy to the feature extraction and fusion units. This feedback loop allows the system to automatically adjust feature weighting and extraction parameters based on actual recognition performance, enabling continuous improvement through automated feedback rather than manual reconfiguration
Solution Approach 2:
The training process dynamically adapts to the user's performance level by adjusting the complexity and difficulty of emotion recognition tasks. The system modifies feature combinations and sensory modalities emphasized in training based on progress, allowing individuals to work at appropriate difficulty levels and improving engagement and efficiency of the training process
Data Source
AI summary
The present invention relates to a system and a method for inspecting an emotion recognition capability using multisensory information, and a system and a method for training emotion recognition using multisensory information. More particularly, the present invention comprises: an output unit for outputting multisensory information including at least one emotional state of a subject person to the outside; an input unit for receiving, from a tested person, emotional state information, which indicates whether the output multisensory information is identical to at least one emotional state of the subject person, on the basis of the output multisensory information; a comparison identification unit for identifying whether the received emotional state information is identical to reference emotional state information corresponding to the multisensory information pre-stored in a storage unit; and a control unit for determining an emotion recognition capability of the tested person according to a result of the identification of the received emotional state information. Due to such a configuration, a system and a method for inspecting an emotion recognition capability using multisensory information and a system and a method for training emotion recognition using multisensory information according to the present invention can determine an emotional state of a subject person by using multisensory information and thus can easily identify an emotion recognition capability of other people.