Technical Field Note #01: Egocentric RGB + IMU Capture Evaluation

Origin Data Lab has released a public technical field note documenting a first-party egocentric RGB + IMU capture evaluation.

The evaluation demonstrates how we validate real-world multimodal capture before scaling field data production for Physical AI and robotics applications.

What We Evaluated

• 1920×1080 egocentric RGB video at 30 fps
• Native and application-level IMU streams
• Measured sensor characteristics
• Video–IMU temporal alignment
• Structured task and interaction metadata
• Data integrity and reproducibility checks

Why This Matters

Real-world robotics data is not only about recording video.

For downstream Physical AI and robotics applications, capture quality, sensor characteristics, synchronization, metadata, and reproducibility all affect whether a dataset can actually be used for training or evaluation.

Our production workflow therefore validates the capture pipeline before scaling field collection.

Public Technical Evidence

The complete technical field note, including the evaluation details and technical evidence, is available on the Origin Data Lab website.

https://origindatalab.io/technical-field-notes/egocentric-rgb-imu-task-sample.html

About Origin Data Lab

Origin Data Lab produces custom real-world multimodal datasets for Physical AI, robotics, embodied AI, and computer vision.

We design and operate field data production around real tasks, real workers, and real operational environments.

Our capabilities include egocentric video, RGB + IMU, stereo and multi-view capture, audio, timestamps, device metadata, metadata engineering, quality control, technical validation, recapture, and structured dataset delivery.

We support project-based field production across Asia and Africa, with operations that can be configured around customer requirements.

Website: https://origindatalab.io