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Course Outline
Foundations of TinyML in Healthcare
- Characteristics of TinyML systems
- Healthcare-specific constraints and requirements
- Overview of wearable AI architectures
Biosignal Acquisition and Preprocessing
- Working with physiological sensors
- Noise reduction and filtering techniques
- Feature extraction for medical time-series
Developing TinyML Models for Wearables
- Selecting algorithms for physiological data
- Training models for constrained environments
- Evaluating performance on health datasets
Deploying Models on Wearable Devices
- Using TensorFlow Lite Micro for on-device inference
- Integrating AI models in medical wearables
- Testing and validation on embedded hardware
Power and Memory Optimization
- Techniques for reducing computational load
- Optimizing data flow and memory usage
- Balancing accuracy and efficiency
Safety, Reliability, and Compliance
- Regulatory considerations for AI-enabled wearables
- Ensuring robustness and clinical usability
- Fail-safe mechanisms and error handling
Case Studies and Healthcare Applications
- Wearable cardiac monitoring systems
- Activity recognition in rehabilitation
- Continuous glucose and biometric tracking
Future Directions in Medical TinyML
- Multi-sensor fusion approaches
- Personalized health analytics
- Next-generation low-power AI chips
Summary and Next Steps
Requirements
- An understanding of basic machine learning concepts
- Experience with embedded or biomedical devices
- Familiarity with Python or C-based development
Audience
- Healthcare professionals
- Biomedical engineers
- AI developers
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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