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 Duration 21 hours

Course Outline

Foundations of TinyML in Healthcare

  • Defining the characteristics of TinyML systems
  • Understanding healthcare-specific constraints and requirements
  • Overview of wearable AI architectures

Biosignal Acquisition and Preprocessing

  • Handling physiological sensors effectively
  • Applying noise reduction and filtering techniques
  • Extracting features from medical time-series data

Developing TinyML Models for Wearables

  • Selecting appropriate algorithms for physiological data
  • Training models within constrained environments
  • Assessing performance using health-specific datasets

Deploying Models on Wearable Devices

  • Leveraging TensorFlow Lite Micro for on-device inference
  • Integrating AI models into medical wearables
  • Testing and validation on embedded hardware

Power and Memory Optimization

  • Strategies for reducing computational load
  • Optimizing data flow and memory utilization
  • Striking a balance between accuracy and efficiency

Safety, Reliability, and Compliance

  • Navigating regulatory considerations for AI-enabled wearables
  • Ensuring system robustness and clinical usability
  • Implementing fail-safe mechanisms and error handling

Case Studies and Healthcare Applications

  • Wearable cardiac monitoring systems
  • Activity recognition in rehabilitation contexts
  • Continuous glucose and biometric tracking

Future Directions in Medical TinyML

  • Approaches to multi-sensor fusion
  • Trends in personalized health analytics
  • Emerging next-generation low-power AI chips

Summary and Next Steps

Requirements

  • Fundamental understanding of machine learning concepts
  • Practical experience with embedded or biomedical devices
  • Proficiency in Python or C-based development

Target Audience

  • Healthcare professionals
  • Biomedical engineers
  • AI developers

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