Challenge

Multimodal human behavior research is only as strong as its weakest link. Researchers must ensure instant synchrony across sensors, data integrity under high throughput, and scalability from small laptop setups to large labs. Traditional solutions often introduced latency, synchronization drift, or risk of data loss — undermining research validity. On top of that, fragmented workflows and IT-heavy setups slowed projects, drained budgets, and limited the scope of experiments.

Solution

The Prophea Data Engine, building on the legacy of D-Lab, delivers a streamlined, future-proof platform:

Instant synchrony & global triggering → millisecond-precise data alignment across modalities, events, and simulators.

Safety in data integrity → high-speed ingestion with redundancy mechanisms ensures no data loss, even in large-scale studies.

Scalable design → runs as efficiently on a laptop in the field as in complex multi-room labs, adapting to project size.

Economic efficiency → a user-friendly GUI, logic-based collaborative workflows, and plug-and-play sensor integration minimize IT overhead, training costs, and setup time.

Simplicity + flexibility → from first setup to long-term deployment, researchers spend time on insights, not troubleshooting.

Impact

Prophea Data Engine enables researchers to enter an entirely new world of multimodal human behavior research:

Speed + safety: data flows instantly, securely, and reliably — even at scale.

Economic value: less IT dependency, faster project setup, and longer equipment lifecycles translate into major cost savings for labs.

New possibilities: synchronized multimodal datasets unlock research that was previously unfeasible — from simulator-based training to large-scale parallel subject studies.

Scientific breakthroughs: integrity and scalability ensure that insights are robust, replicable, and globally comparable.

Example

Imagine a sports science lab running a team-level performance study. With older tools, synchronizing eye tracking, physiological sensors, and video across 10 athletes simultaneously was nearly impossible — risking data gaps, drift, or weeks of post-processing.

With Prophea ^{data engine}

All data streams are captured with instant synchrony and guaranteed integrity.

Researchers can trigger global events (e.g., whistle blows, play start) across all systems.

Setup takes hours instead of weeks, with sensors added via plug-and-play.

The study scales easily from a single training laptop to a large, multi-room lab without reengineering IT.

The result: valid, high-quality data at unprecedented speed and reliability – and the ability to explore new, large-scale research questions that were once out of reach.

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