Mobile & Tablet
Per-Device Custom rPPG Model Training
We train device-specific rPPG models for your exact smartphone or tablet camera hardware — front-facing selfie cameras, depth sensors, and multi-lens arrays. Every model is built from scratch for the sensor in your target device.
Custom preprocessing handles auto-exposure, rolling shutter, and OIS artifacts unique to each device. Generic rPPG SDKs treat all mobile cameras the same — we build models that account for the specific signal characteristics of yours.
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Core Capabilities
What We Build
Per-Device Model Training
Individual rPPG models trained on data from your target device hardware. An iPhone 15 Pro camera behaves differently from a Samsung Galaxy S24 — we train for yours specifically.
Auto-Exposure Compensation
Custom preprocessing that handles the aggressive auto-exposure algorithms on mobile cameras, which cause brightness fluctuations that corrupt rPPG signals in generic SDKs.
Rolling Shutter Correction
Preprocessing that compensates for rolling shutter artifacts in CMOS sensors — the temporal skew across frame rows introduces systematic error that we model and remove.
Native iOS & Android SDKs
Production-ready Swift (iOS) and Kotlin (Android) SDKs with full API documentation, sample apps, and integration guides. Not cross-platform wrappers — true native performance.
OIS Artifact Removal
Optical image stabilization creates micro-movements that generic rPPG algorithms interpret as physiological signal. Our preprocessing isolates and removes OIS-induced artifacts.
White-Label UI Components
Pre-built measurement screens, progress indicators, and results displays that match your app's design system. Fully customizable colors, fonts, animations, and layout.
Built for Mobile Hardware
Supported Platforms
iOS 15+, Android 10+, iPadOS 15+, Android tablets
Camera Requirements
Front-facing camera, minimum 720p, 24fps+
SDK Languages
Swift for iOS, Kotlin for Android, TypeScript for React Native bridge
Measurement Time
30 seconds default, configurable 15-60 seconds
SDK Size
Under 15MB for both iOS and Android
Output
Native objects, JSON, FHIR R4 Observation resources
Mobile & Tablet rPPG FAQ
Common questions about custom rPPG for mobile and tablet devices
Every smartphone camera has different sensor characteristics, ISP processing, auto-exposure behavior, and OIS implementation. A model trained on iPhone data performs measurably worse on Samsung and vice versa.
We train models for any device. Enterprise customers with specific device fleets (e.g., company-issued iPads, specific Android tablets) get models tuned exactly for those devices.
Truly native. Swift for iOS, Kotlin for Android. We offer a React Native bridge for cross-platform apps, but the core inference runs natively for maximum performance.
We deliver customizable UI components (SwiftUI/Kotlin Compose) that you style with your brand assets. Measurement screens, progress animations, and results displays — all configurable via a theming API.
Heart rate, respiratory rate, HRV (SDNN, RMSSD), blood pressure trends, SpO2, and stress level. All from the front-facing camera in a 30-second measurement.
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