Course Outline

Foundations of TinyML in Healthcare for Government

  • Characteristics of TinyML Systems for Government Use
  • Healthcare-Specific Constraints and Requirements for Government Applications
  • Overview of Wearable AI Architectures for Government

Biosignal Acquisition and Preprocessing for Government

  • Working with Physiological Sensors in a Government Context
  • Noise Reduction and Filtering Techniques for Government Use
  • Feature Extraction for Medical Time-Series Data for Government Applications

Developing TinyML Models for Wearables for Government

  • Selecting Algorithms for Physiological Data in a Government Setting
  • Training Models for Constrained Environments in Government Operations
  • Evaluating Performance on Health Datasets for Government Use

Deploying Models on Wearable Devices for Government

  • Using TensorFlow Lite Micro for On-Device Inference in a Government Context
  • Integrating AI Models in Medical Wearables for Government Applications
  • Testing and Validation on Embedded Hardware for Government Use

Power and Memory Optimization for Government

  • Techniques for Reducing Computational Load in a Government Setting
  • Optimizing Data Flow and Memory Usage for Government Applications
  • Balancing Accuracy and Efficiency for Government Use

Safety, Reliability, and Compliance for Government

  • Regulatory Considerations for AI-Enabled Wearables in a Government Context
  • Ensuring Robustness and Clinical Usability for Government Applications
  • Fail-Safe Mechanisms and Error Handling for Government Use

Case Studies and Healthcare Applications for Government

  • Wearable Cardiac Monitoring Systems for Government Use
  • Activity Recognition in Rehabilitation for Government Applications
  • Continuous Glucose and Biometric Tracking for Government Use

Future Directions in Medical TinyML for Government

  • Multi-Sensor Fusion Approaches for Government Applications
  • Personalized Health Analytics for Government Use
  • Next-Generation Low-Power AI Chips for Government Operations

Summary and Next Steps for Government

Requirements

  • A foundational knowledge of machine learning concepts for government applications
  • Practical experience with embedded or biomedical devices
  • Proficiency in Python or C-based development

Audience

  • Healthcare professionals
  • Biomedical engineers
  • Artificial intelligence developers for government projects
 21 Hours

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