IoT Sensor Emotion Analytics for Audio-Visual Content Alignment
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Solution Overview
Problem
Current systems lack the ability to capture and analyze user emotions in real-time during audio-visual content consumption, failing to provide actionable feedback for improving the user experience by modifying future content frames based on emotional responses.
Innovation Solution
A method utilizing IoT sensors to capture user physiological data, convert emotions into connotations using emotional vector analytics and supervised machine learning, and generate suggestions for modifying video frames to better align with the intended emotional response of the content creator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time emotion capture and analysis systems are implemented, then user experience enhancement and content alignment improve, but system complexity and device requirements increase
Solution Approach 1:
The system implements real-time feedback by capturing user physiological data through IoT sensors, analyzing emotions via machine learning models, and generating actionable suggestions for content modification. This closed-loop feedback mechanism enables continuous improvement of content alignment with user emotional responses, resolving the contradiction between enhanced productivity and system complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer that bridges raw sensor data and content modification decisions. This intermediary system includes emotional vector analytics and supervised machine learning models that translate complex physiological signals into actionable connotations, simplifying the overall system architecture while maintaining high analytical accuracy.
2Measurement precision
If emotional vector analytics and machine learning techniques are applied, then emotion conversion accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models and establishing emotional vector mappings before actual content consumption. This pre-computation enables rapid real-time emotion conversion during content viewing, reducing processing time while maintaining high accuracy through the pre-established analytical frameworks.
Solution Approach 2:
The patent optimizes processing parameters by adjusting the complexity of analytical models based on data availability and urgency. The system dynamically changes processing parameters such as model selection, data sampling rates, and analysis depth to balance accuracy requirements with processing time constraints in real-time scenarios.
3Reliability
If IoT sensors and real-time data capture are deployed, then emotional feedback reliability improves, but device cost and infrastructure requirements increase
Solution Approach 1:
The system employs universal IoT sensor platforms that can capture multiple types of physiological data (heart rate, respiratory rate, movement, neural signals) through a single integrated infrastructure. This multi-functional approach ensures reliable emotional feedback while reducing infrastructure requirements by using standardized, versatile sensor devices rather than specialized equipment for each measurement type.
Data Source
AI summary
In an approach for enhancing an experience of a user listening to and/or watching an audio-visual content by modifying future audio and/or video frames of the audio-visual content, a processor captures a set of sensor data from an IoT device worn by the first user. A processor analyzes the set of sensor data to generate one or more connotations by converting the emotion using an emotional vector analytics technique and a supervised machine learning technique. A processor scores the one or more connotations on a basis of similarity between the emotion exhibited by the first user and an emotion expected to be provoked by a second user. A processor determines whether a score of the one or more connotations exceeds a pre-configured threshold level. Responsive to determining the score does not exceed the pre-configured threshold level, a processor generates a suggestion for the producer of the audio-visual content.