Emotional Imaging Composer for Real-Time Biosignal Visualization

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Solution Overview

Problem

Current systems lack the ability to effectively convert and utilize real-time biosignals into visual or environmental responses that accurately represent a person's emotional state for applications such as psychological therapies or dramatic performances.

Innovation Solution

The Emotional-Imaging Composer system uses a combination of biosignal sensors, machine learning models, and interactive multimedia applications to record and analyze biosignals, predicting emotions and generating corresponding visual or lighting effects on a graphical user interface, allowing for real-time emotional representation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If real-time biosignal conversion to visual responses is implemented, then emotional state representation capability is improved, but system complexity increases

Engineering Contradiction:

Improveemotional state representation capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the complex task of emotional representation into distinct functional modules: biosignal acquisition module, machine learning analysis module, and visual/environmental output module. This segmentation allows each component to be developed and optimized independently while maintaining overall system functionality.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces machine learning models as an intermediary layer between biosignal sensors and visual/environmental outputs. This intermediary processes raw biosignals to infer emotional states, then translates these states into appropriate visual or environmental responses, thereby simplifying the overall system architecture.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If machine learning models are used to predict emotions from biosignals, then emotion detection accuracy is improved, but processing time increases

Engineering Contradiction:

Improveemotion detection accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-training machine learning models offline with extensive biosignal datasets. This pre-training establishes robust emotion prediction capabilities that can then be applied in real-time with minimal processing delay, as the heavy computational lifting has already been completed during the training phase.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements a tiered processing approach where the machine learning model first performs rapid preliminary classification of emotional states, then applies more computationally intensive analysis only when needed for ambiguous cases or detailed emotional profiling, thereby balancing accuracy with processing speed.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11531394B2Systems and methods for emotional-imaging composer

Publication Date: 2022.12.20 EMOTIONAL IMAGING INC

AI summary

Systems and methods for Emotional-Imaging Composer are disclosed. The method may include recording a real-time biosignal from a plurality of biosignal sensors. The method may further include determining an emotion that is associated with the real-time biosignal. The method may further include outputting a display feature corresponding to the emotion, wherein the display feature is a lighting effect on a graphical user interface.