Sensor Data Quality Upgrade Framework

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

Patent Information: