Biometric Replay Server for Training Session Synchronization
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Solution Overview
Problem
Current virtual reality and mixed reality training systems struggle to simulate real-world stimuli effectively and face challenges in analyzing biometric data for trainee performance, particularly in large or complex environments, due to delays in data processing and limited interoperability between biometric hardware and software.
Innovation Solution
A system that captures and processes biometric, motion capture, and video data during training sessions to generate a 3D virtual replay synchronized with near real-time biometric analysis, using a scanner device to create a virtual mesh of the training environment, wearable biometric sensors to record psychophysiological indicators, and a replay server to edit and stitch the data for seamless representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex processing is performed to extract indicators from multiple biometric measurement data, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by capturing and storing all raw biometric measurement data during the training session in real-time, rather than collecting and processing data after the session. This allows the comprehensive data to be available immediately for analysis, eliminating the time delay between training completion and data processing while maintaining high measurement precision through complete data capture.
Solution Approach 2:
The system replaces manual or sequential biometric data processing with automated computational processing. Multiple biometric data streams from different sensors are automatically integrated, synchronized, and analyzed by computer algorithms, significantly reducing the time required to extract behavioral indicators while improving measurement precision through comprehensive multi-source data analysis.
2Measurement precision
If multiple biometric hardware sensors are used to record comprehensive measurement data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system implements a universal data capture platform that can interface with multiple types of biometric sensors (eye tracking, ECG, respiration, temperature, GSR, FEA) through standardized protocols. This multi-functional architecture allows comprehensive biometric monitoring while managing device complexity through unified data collection and processing mechanisms that handle diverse sensor inputs consistently.
Solution Approach 2:
The system introduces an intermediary data management layer that sits between multiple biometric hardware sensors and the analysis engine. This intermediary component standardizes data formats, synchronizes timing across different sensors, and manages data integration, thereby reducing the complexity burden of handling multiple sensor types while maintaining high measurement precision through coordinated data collection.
3Measurement precision
If biometric data is processed after training completion, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary data capture and continuous processing during the training session itself, rather than waiting until completion. Biometric measurements are continuously recorded and preliminary analysis is performed in real-time, allowing immediate extraction of behavioral indicators while maintaining measurement precision through comprehensive data collection throughout the entire training experience.
Data Source
AI summary
Systems and methods for capturing a training environment, processing a recorded training session and generating a 3D virtual replay of a training session is provided. The system includes a server for generating a virtual twin of the training environment captured by a scanner device. The server further processes data, from a plurality of biometric hardware sensors worn by a trainee as they perform activities in the training environment, to extract psychophysiometric indicators of the trainee's behavior/response. The gaze direction, position and body movement of the trainee is also recorded by motion capture sensors during the training session. The server is further configured to generate a post-training session replay of a bio twin (virtual twin) of the trainee in the virtual environment, and synchronize the replay with the biometric sensor data and extracted insights and indicators of behavior/response of the trainee in the training environment.