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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