Open-Source Cybersecurity Project by Paulo Bião — Smart infrastructure monitoring system simulating IoT/OT security and anomaly detection for EB2-NIW portfolio.
Objective
Develop an open-source system for physiotherapy data analytics, motion tracking, and rehabilitation prediction using AI — promoting smarter and safer digital health experiences.
This repository is portfolio-grade evidence of applied AI and data analytics for the EB2-NIW portfolio.
- Real-time posture tracking and motion capture (OpenPose + MediaPipe)
- AI-driven injury prediction and recovery timeline estimation
- Secure storage (AES-256) for anonymized patient data
- REST API with FastAPI + Docker + CI
- CSV data ingestion and analytics dashboard (Plotly/Dash)
Python 3.11 • FastAPI • TensorFlow • scikit-learn • MediaPipe • Plotly • Docker • GitHub Actions
git clone https://github.com/paulobiao/PhysiDigital.git
cd PhysiDigital
pip install -r requirements.txt
uvicorn src.main:app --reload
docker build -t physidigital:0.1.0 .
docker run -p 8080:8080 physidigital:0.1.0
🔒 Security & Compliance Notes
• Example-only dataset; no real PHI.
• Demonstrates HIPAA principles: confidentiality, integrity, and accountability.
• Encryption keys are generated locally for testing and education purposes.
⸻
🧩 EB2-NIW Mapping (How this supports your petition)
• National Importance: strengthens U.S. healthcare cybersecurity and privacy compliance.
• Well Positioned: demonstrates applied knowledge in encryption, RBAC, and auditing.
• On Balance: contributes open-source resources to improve data protection in healthcare.
⸻
📌 Next Steps (good for portfolio)
• Add JWT authentication and key rotation.
• Integrate FHIR schema validation for health data.
• Publish Grafana dashboards for audit event visualization.
---
## 🏙️ PHYSIDIGITAL™ — Copie tudo abaixo e cole no README.md do repositório *PhysiDigital*
```markdown
# PhysiDigital™ – Infrastructure & IoT Threat Monitoring System (Open Source)
**Objective:** Monitor IoT and OT (Operational Technology) environments to detect physical and digital anomalies through AI-driven analytics, strengthening cybersecurity in critical infrastructure sectors.
> This repository is designed as **portfolio-grade evidence** of infrastructure security expertise for **EB2-NIW**. It demonstrates anomaly detection, IoT telemetry analysis, and secure data pipelines for industrial systems.
---
## ✨ Features
- **IoT/OT telemetry collection** using FastAPI and MQTT integration
- **AI-based anomaly detection** (scikit-learn model demo)
- **Secure logging** and event correlation system
- **Dockerized** for easy deployment across edge or cloud systems
- **Continuous Integration** with GitHub Actions CI
- MIT License
---
## 🏗️ Architecture
```mermaid
flowchart LR
A[Sensors / IoT Devices] -->|MQTT / HTTPS| B[PhysiDigital Collector]
B --> C[AI Anomaly Engine (scikit-learn)]
B --> D[(PostgreSQL / InfluxDB)]
B --> E[Monitoring Dashboard]
🚀 Quick Start
Using Docker (recommended)
docker compose up --build
# API at http://localhost:8080/docs
Local (Python 3.11+)
python -m venv .venv && source .venv/bin/activate # (Windows: .venv\Scripts\activate)
pip install -r requirements.txt
uvicorn physidigital.main:app --reload --port 8080
uvicorn physidigital.main:app --reload --port 8080
🧪 Tests
pytest -q
📁 Project Layout
src/
physidigital/
main.py # FastAPI app
sensors.py # IoT data ingestion
analytics.py # AI anomaly detection
models.py # data schemas
tests/
test_sensors.py
test_analytics.py
data/
sample_signals.csv
docs/
system_topology.md
.github/workflows/ci.yml
Dockerfile
docker-compose.yml
requirements.txt
LICENSE
README.md