Local-first AI health platform
Turn a normal webcam into a health and wellbeing platform.
HealthCam runs camera-based analyzers locally, connects them to cloud dashboards, and turns webcam signals into modular health and wellbeing features.
Started as a validated B.Sc. thesis prototype for camera-based BPM estimation, HealthCam is evolving into a local/cloud platform for computer vision health analyzers, plugin modules, telemetry pipelines, and dashboard-ready insights.
Today’s wellbeing session
72 min focused
Screen presence
Processed35 min in front of screen
Suggested action
Break recommendedTake a 2-minute eye break
Example analyzer
rPPG / BPM
Processing mode
Local-first
Module store concept
Add new health and wellbeing analyzers as the platform evolves.
Platform overview
Local analysis, cloud product experience, installable modules.
HealthCam is designed as a platform rather than a single analyzer. The local machine handles sensitive and hardware-close processing, while the cloud provides dashboards, configuration, and long-term product experience.
Local machine
Captures webcam input, runs local services, and executes computer vision analyzers close to the user’s hardware.
- Webcam capture
- Local analyzers
- Service orchestration
Cloud dashboard
Handles authentication, telemetry storage, insight tables, dashboards, configuration, and product experience.
- Authentication
- Telemetry storage
- Dashboard insights
Plugin ecosystem
Allows new analyzers to become installable modules with their own service, telemetry schema, transformations, and dashboards.
- Plugin manifest
- SQL transformations
- Module dashboards
Modules
From research validation to practical wellbeing tools.
HealthCam started by validating camera-based BPM estimation. The next step is turning that foundation into useful modules for everyday computer wellbeing.
Camera-based BPM research
HealthCam started from a working rPPG thesis prototype that estimated heart rate from normal webcam input under real computer conditions.
Eye-strain support
A practical wellbeing module designed to suggest breaks based on actual screen presence rather than fixed timers alone.
Presence-aware sessions
HealthCam can distinguish between open-laptop time and actual time spent in front of the screen.
Stress-pattern awareness
Future modules may combine physiological signals, presence, and work-session patterns to help users notice stress and recovery trends.
How it works
A complete path from webcam signal to dashboard insight.
HealthCam connects local computer vision services to cloud-side telemetry processing, allowing analyzer outputs to become product features instead of isolated research scripts.
Camera input
A normal webcam captures signals from the user’s environment.
Local processing
Computer vision analyzers run locally whenever possible, close to the hardware.
Telemetry pipeline
Analyzer outputs are sent as structured telemetry into the cloud pipeline.
Dashboard insights
SQL transformations generate dashboard-ready insights from raw telemetry.
Processing mode
Local-first
Camera input
Handled on the user device where possible.
Analyzer execution
Runs as local services close to the hardware.
Cloud dashboard
Used for visualization, settings, and product experience.
Privacy direction
Camera data should stay close to the user.
HealthCam is designed around local-first processing because camera input can be sensitive, latency-dependent, and hardware-specific. The cloud supports the product experience, but raw webcam processing does not need to be centralized by default.
Platform status
From validated prototype to first user-facing modules.
HealthCam already supports the core local/cloud flow: webcam capture, independent local services, analyzer execution, telemetry ingestion, data processing, and dashboards. HealthCam 3.0 upgrades that foundation into a plugin-based platform so new health and wellbeing analyzers can become installable modules.
Already working
- Local webcam capture
- rPPG/BPM analyzer integration
- Independent local microservices
- Automatic service lifecycle management
- Cloud telemetry ingestion
- Raw telemetry storage
- Dashboard data processing
- End-to-end dashboard flow
3.0 platform upgrade
- Plugin-based analyzer system
- Manifest-based service discovery
- Plugin-compatible orchestration
- Module-specific telemetry schemas
- Installable analyzer modules
Next product modules
- Eye-strain break module
- Presence-aware work sessions
- First user testing
- Additional wellbeing analyzers
- Plugin marketplace direction
Engineering
Built as a real local/cloud computer vision platform.
Behind the product interface, HealthCam uses a local agent, independent analyzer services, service lifecycle management, manifest-based discovery, raw telemetry storage, SQL transformations, and cloud dashboard integration.
Founder note
Built by Diego Turri as a founder-engineer project.
I started HealthCam as my B.Sc. thesis prototype and continued evolving it into a modular platform for camera-based health and wellbeing analyzers. The goal is to make research-level computer vision easier to test, integrate, and turn into useful products.