Custom Model Capabilities
Technical depth across every stage of camera-specific rPPG model development
Model Training and Optimization
Core capabilities that define custom rPPG model performance
Sensor-Specific Signal Extraction
We characterize your camera's noise floor, quantum efficiency curve, and Bayer pattern response, then train models that maximize the physiological signal your specific sensor can capture. The result is substantially higher signal-to-noise ratio than any generic model.
Multi-Spectral Model Support
Beyond standard RGB, we build models for NIR, SWIR, thermal, and hybrid sensor arrays. Each spectral band offers different physiological information, and our training pipeline extracts the maximum from whatever your hardware provides.
Environment-Adaptive Training
Your device operates in specific lighting conditions — automotive cabins, office environments, outdoor kiosks, or ambient-lit bedrooms. We train models against representative environmental data so performance is robust where it matters, not just in lab settings.
Configurable Vital Sign Outputs
Heart rate, HRV, respiratory rate, SpO2 estimation, stress indicators, and blood pressure trends. You choose which vitals your product needs, and we architect a model pipeline that delivers exactly those outputs at the quality level your use case demands.
Model Training and Optimization
Deployment and Integration
Production-ready delivery designed for embedded hardware teams
Compute-Budgeted Inference
Every model is optimized for your target hardware. We profile inference against your SoC, apply quantization and pruning to meet your latency and power targets, and validate performance at the edge before delivery. No cloud fallback required.
SDK and Binary Delivery
Models ship as production-ready artifacts — ONNX, TFLite, CoreML, or custom binary formats depending on your runtime. Comprehensive integration documentation and reference implementations accelerate your team's path to production.
Fully Offline Architecture
All inference runs on-device with zero network dependency. Biometric data never leaves the hardware. This architecture satisfies automotive functional safety requirements, healthcare data regulations, and enterprise privacy mandates by design.
Deployment and Integration
Custom-Trained vs Generic rPPG Models
| Feature | TryVitalsApp | Traditional Methods |
|---|---|---|
| Camera-Specific Optimization | ||
| IR/Thermal Sensor Support | ||
| Edge Compute Profiling | ||
| On-Device Inference Only | Rare | |
| Multi-Vital Output Pipeline |
Experience TryVitalsApp Today
See what contactless vitals monitoring can do — try it free.