How YAQEEN turns sensor data into understanding
From vital signs to meaningful insight.
- 1
Wearable Sensors
- Heart rate
- Blood oxygen
- Temperature
- Movement
- 2
Signal Quality Analysis
- Filters noise
- Detects motion artifacts
- Checks reliability
- 3
Personal Baseline Engine
- Learns Salman's usual patterns
- 4
Multimodal Pattern Analysis
- Analyzes relationships between multiple signals across time
- 5
Health-State Reasoning
- Stable state
- Temporary activity
- Sustained unusual change
- 6
Explainable AI
- Identifies which signals contributed most
- 7
Caregiver Insight
- Understandable explanation
- Recommended level of attention
The AI does not simply ask whether one value crossed a threshold. It asks whether Salman's overall physiological pattern has meaningfully changed.
YAQEEN Health Intelligence Engine
Inputs · reasoning pipeline · caregiver outputs
Inputs
- Heart Rate
- SpO2
- Temperature
- Movement
- Current activity state
- Signal trends
- Duration
- Personal baseline
Process
- 1Signal Quality
- 2Feature Extraction
- 3Baseline Comparison
- 4Multimodal Pattern Detection
- 5Health-State Classification
- 6Explainability Engine
Outputs
- Health State
- Confidence
- Contributing Signals
- Caregiver Explanation
- Recommended Attention Level
Noisy samples are re-verified before they can influence an interpretation.
Demo Control Center
Salman is sleeping.