Challenge
Traditional human factors studies often rely on sequential single-user sessions, leading to time-consuming data collection, poor scalability, and delayed insights—especially in complex, multi-user or human-machine interaction scenarios.

Solution
Prophea^{data engine} enables parallel multi-subject recording with a unified timecode architecture. It captures synchronized streams (e.g., video, eye tracking, biometrics, and system logs) across multiple participants in real time—scalable from a single user to entire control room teams.

Impact
– Drastically reduce post-processing and synchronization time.
– Prophea.X enables real-time pattern detection and cross-subject analysis.
– Accelerate testing and iteration cycles for faster, data-driven decisions in automotive, aerospace, and multi-agent systems research

Challenge
Eye tracking is essential for understanding attention and cognitive behavior—but integrating it across multiple participants and syncing it with other data streams (like video, biometrics, or simulator inputs) is often technically complex and time-consuming. Traditional setups struggle to handle multi-subject scenarios reliably.

Solution
Prophea^{data engine} supports simultaneous multi-subject eye tracking within its unified recording architecture. Whether using screen-based or wearable systems, gaze data from multiple participants is captured, synchronized, and visualized in real time—alongside other behavioral and environmental data.
Particular Features:
– Record and analyze eye tracking data from multiple users at once.
– Real-time gaze monitoring and visualization for each subject.
– Automatic synchronization with all other data streams (video, audio, biometrics, simulator inputs)
– Scalable for collaborative or comparative studies involving teams or group interactions

Impact
– Enable complex multi-user studies with synchronized eye tracking and contextual data.
– Analyze interpersonal dynamics, shared attention, and collaboration patterns.
– Reduce setup time and eliminate post-processing of multi-participant gaze data

Drive deeper insight into real-world behavior in group-based environments like control rooms, simulations, or co-piloting scenarios

The Challenge:
Manual Integration Slows Research Down
Multimodal observation relies on data from various sources—each with its own format, frequency, and synchronization demands. Beyond the complexity of triggering multiple systems, traditional workflows depend heavily on manual processes, which:
– Create delays in analysis
– Increase the risk of data misalignment
– Limit the availability of real-time insights
– Complicate studies involving dynamic, real-time interaction

The Solution:
Direct Simulator Connectivity with Prophea^{data engine}
Prophea^{data engine} enables native, direct integration with simulation environments through standardized interfaces such as TCP/IP, CAN, ROS, or proprietary SDKs. With this capability:
– Simulation data streams are captured live and automatically synchronized with behavioral and sensor data
– No more post-hoc integration or custom data parsing
– Researchers can define triggers, markers, and events in real time, based on simulator states
– Both open-loop and closed-loop testing workflows are supported

The Impact
Real-Time Insight, Richer Data, Faster Decisions
– Immediate access to fully integrated datasets accelerates iteration and validation cycles
– Enables complex scenario testing with multiple participants or dynamic machine states
– Improves data fidelity, enhancing the reliability of research findings
– Streamlines workflows and significantly reduces time-to-insight—especially critical in safety-sensitive domains like automotive, aerospace, and healthcare simulation

Challenges
Behavior research labs often rely on fragmented tools and manual processes. Managing multiple data streams—from video and audio to sensors and annotations—can be time-consuming, error-prone, and hard to replicate, limiting the reliability and speed of behavioral insights.
Coordinating across multiple users or research teams compounds these challenges, slowing progress and risking inconsistent data quality.

Solution
Prophea^{data engine} unifies behavior research into one integrated platform designed for multi-user environments and collaborative workflows. It supports synchronized recording and real-time monitoring across diverse data streams—including video, eye tracking, physiological sensors, and more—while enabling teams to work simultaneously and share data securely.

Experience
– Centralized experiment design and control with multi-user access
– Real-time annotation, event marking, and sensor synchronization across participants
– Scalable from individual studies to complex, multi-subject research
– Collaborative data management with customizable access rights
– Immediate access to structured, export-ready datasets for all team members

Impact
Enter streamlined, collaborative workflows that drastically accelerate research cycles through:
– Consistently high-quality, synchronized data across users and sites.
– Reduced setup complexity and enhanced reproducibility.
– Empowered teams can iterate faster, driving deeper insights through shared analysis.