This patent discloses a Sensor Quality Upgrade Framework (SQUF) that employs trained machine-learning models – such as stochastically optimized artificial neural networks – to enhance real‑time, low-quality sensor data (e.g., from wearable devices) into high‑fidelity, medically or otherwise useful output comparable to that from gold‑standard sensors.
Background:
The invention arises from the growing field of mobile health (M‑Health) – which blends portable, wireless devices (like smartphones, PDAs, and wearable sensors) with healthcare delivery to decentralize care and reduce costs. However, while these devices are proliferating, many wearable sensors (e.g., for heart rate variability) often produce data that lack the accuracy needed for medical diagnosis. This creates a clear need for a method or system capable of upgrading low-quality sensor data to a clinically useful standard, which is exactly what this invention addresses.
Applications:
Mobile and Wearable Health Monitoring
Remote Patient Monitoring
Fitness and Wellness Tracking
Clinical Decision Support
Resource-Limited or Rural Healthcare
Medical Research and Trials
Personalized Health Insights
Advantages:
Improves Data Quality from Low-Cost Sensors
Enables Real-Time Data Enhancement
Reduces Healthcare Costs
Increases Accessibility to Quality Health Data
Flexible Model Training Framework
Supports Broad (Medical) Application Areas
Facilitates Mobile Health (M-Health) Initiatives
Status: issued patent #12,373,682