Neural Network Engagement Classification via Video and Bio Telemetry Fusion
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Solution Overview
Problem
Current methods for analyzing learner engagement in online sessions rely heavily on assumptions and lack automation for quality assurance, particularly in virtual instructor-led courses, and are not platform-agnostic.
Innovation Solution
A system that uses a convolutional neural network (CNN) long short-term memory (LSTM) neural network to capture visual and bio telemetry data from learners, enabling the classification of engagement levels by correlating facial patterns with bio data, and deploying a virtual agent to record and analyze learner engagement across various online platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for analyzing learner engagement are used, then the analysis process is simple, but the accuracy and reliability of engagement classification is poor
Solution Approach 1:
The patent combines multiple data sources (video recordings, bio telemetry data, facial patterns) and multiple analysis techniques (CNN for visual data, LSTM for temporal patterns) into a unified engagement classification system. This integration of heterogeneous data streams and methodologies resolves the contradiction by achieving high measurement precision through comprehensive data fusion while managing system complexity through coordinated multi-component architecture.
Solution Approach 2:
The engagement analysis system functions as a composite structure combining different types of data (visual, physiological, temporal) and different processing approaches (convolutional neural networks, recurrent neural networks). This composite approach enables accurate engagement classification by leveraging the complementary strengths of multiple data types and analysis methods, resolving the trade-off between precision and complexity.
2Productivity
If automated analysis systems are implemented, then productivity and quality assurance improve, but the device complexity increases
Solution Approach 1:
The system implements automated self-service capabilities by using neural networks to autonomously process video and bio telemetry data, classify engagement levels, and generate analytics without requiring manual intervention. This automation resolves the contradiction by dramatically improving productivity and quality assurance efficiency while the modular architecture manages the inherent complexity through systematic decomposition of tasks.
Solution Approach 2:
The patent replaces manual engagement analysis (mechanical/human process) with automated neural network-based classification (information processing system). This substitution resolves the contradiction by eliminating the need for manual quality assurance while implementing sophisticated automated systems that handle complexity through algorithmic processing rather than human effort.
3Adaptability or versatility
If platform-specific analysis methods are used, then the analysis can be tailored to specific platforms, but the adaptability across different platforms is reduced
Solution Approach 1:
The system achieves universality by designing a platform-agnostic architecture that can process and analyze engagement data across multiple online learning platforms. The neural network model is configured to handle diverse data formats and sources while maintaining consistent engagement classification standards, resolving the contradiction by enabling broad platform compatibility without sacrificing analysis precision through standardized processing pipelines.
Solution Approach 2:
The system segments the engagement analysis process into distinct functional components (video processing, bio telemetry analysis, neural network classification) that can operate independently and be adapted to different platforms. This segmentation resolves the contradiction by allowing the core analysis engine to remain platform-agnostic while enabling customization at the data collection and preprocessing level for each specific platform.
Data Source
AI summary
A system can identify time-series bio telemetry data that corresponds to a first user of an online session, wherein the time-series bio telemetry data identifies a first user engagement of the first user. The system can identify a first video recording of the online session, wherein the first video recording is representative of a first video of the first user. The system can input the time-series bio telemetry data and the first video recording as a first input to a neural network model to produce a trained neural network model, wherein the trained neural network model is configured to classify a second user engagement from a second video recording representative of a second video of a second user. The system can classify the second user engagement of the second user comprising inputting the second video recording as a second input to the trained neural network model.