How Custom Model Building Works
A structured process from hardware characterization to production-ready model delivery
Simple Steps to Get Started
Measuring your vitals takes less than a minute
Hardware Characterization
We start by profiling your camera hardware — sensor specifications, optics, noise characteristics, spectral response, and the compute constraints of your target SoC. This baseline defines the model architecture and training strategy.
Data Collection and Annotation
Using your actual camera hardware, we capture training data under representative deployment conditions. Combined with our existing rPPG dataset library and ground-truth reference devices, this builds the foundation for camera-specific model training.
Model Training and Optimization
We train and fine-tune rPPG models against your hardware's specific characteristics, applying transfer learning from our model library. Iterative optimization targets your exact compute budget, latency requirements, and vital sign output specifications.
Validation and Delivery
Trained models undergo rigorous testing on your target hardware under real-world conditions. We deliver production-ready model binaries, integration SDKs, and technical documentation. Ongoing support ensures performance through your product lifecycle.
Ready to Scope a Custom Model Build?
Tell us about your hardware and use case. Our engineering team will outline a custom model build plan tailored to your device.