CareSystem AI is a comprehensive, real-time exercise tracking and patient monitoring platform developed for the Centre for Community, Clinical and Applied Research Excellence (CCCARE). By integrating advanced computer vision, biometric security, and live medical sensor data, this system automates the supervision of physical rehabilitation for patients with complex clinical needs.
Traditional rehabilitation monitoring is resource-intensive, often requiring one-on-one supervision to ensure patient safety and form accuracy. CareSystem AI empowers CCCARE by:
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Scaling Clinical Expertise
Automated form correction allows a single therapist to oversee multiple patients simultaneously without a drop in quality of care. -
Objective Progress Tracking
Replaces manual logs with high-precision, timestamped data on repetitions, joint angles, and physiological responses. -
Frictionless Workflow
Biometric identification automatically loads patient history, medications, and specific exercise protocols upon entry.
CCCare.Video.mp4
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DeepFace & FaceNet512
Identifies patients using one-way neural embeddings — mathematical fingerprints that cannot be reversed into photos. -
Photo-less Database
Stores numerical vectors rather than raw images, supporting privacy-by-design healthcare compliance.
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Skeletal Landmark Tracking
Uses MediaPipe to track 33 body points and calculate joint angles to differentiate between complex movements. -
State Machine Rep Engine
Custom logic combined with Exponential Moving Averages ensures repetitions are only counted when full range of motion is achieved.
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Audio Coaching
Multilingual voice feedback provides actionable guidance (e.g., “Go lower”, “Keep your back straighter”). -
Visual Debouncing
Temporal smoothing ensures stable, flicker-free feedback for patients and clinicians.
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WebSocket Streaming
Streams real-time heart rate and oxygen saturation data from sensors to a React dashboard. -
Edge Processing
Video analysis occurs locally while only anonymized analytics are synced to cloud storage.
CareSystem AI acts as a second set of eyes for therapists:
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Heart Rate Spike Detection
Alerts triggered when patient heart rate exceeds configured safety thresholds. -
Oxygen Level Monitoring
Alert triggered when abnormal oxygen levels are detected. -
Full-Body Presence Validation
Rep counting pauses if critical skeletal landmarks are lost.
- Node.js
- Python 3.9+
- Webcam
- Optional: medical sensor hardware
npm install
npm run devnpm install
npm run devWe plan to evolve CareSystem AI from movement tracking toward predictive rehabilitation intelligence.
- AR overlays with interactive virtual targets
- Game-like progression systems to increase adherence
- Personalized motivational feedback loops
- Machine learning models trained on historical patient data
- Plateau detection and automated intervention suggestions
- Adaptive exercise intensity recommendations
- Session metric summarization
- EHR-ready export pipelines
- Multi-clinic deployment scaling
- Role-based clinician dashboards
- Secure interoperability with hospital systems