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 Duration 21 hours

Course Outline

Core Principles of TinyML in Healthcare

  • Key attributes of TinyML systems
  • Specific limitations and needs within the healthcare sector
  • An introduction to wearable AI architectures

Acquiring and Preparing Biosignals

  • Interaction with physiological sensors
  • Methods for noise reduction and signal filtering
  • Extracting meaningful features from medical time-series data

Building TinyML Models for Wearables

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

Deploying Models onto Wearable Hardware

  • Leveraging TensorFlow Lite Micro for on-device inference
  • Integrating AI models into medical wearable devices
  • Conducting testing and validation on embedded hardware

Optimizing Power and Memory Usage

  • Strategies to lower computational demands
  • Enhancing data flow and memory utilization
  • Achieving a balance between model accuracy and efficiency

Safety, Reliability, and Compliance

  • Regulatory frameworks for AI-powered wearables
  • Safeguarding system robustness and clinical usability
  • Implementing fail-safe mechanisms and error management

Case Studies and Medical Use Cases

  • Systems for continuous cardiac monitoring
  • Activity detection in rehabilitation settings
  • Ongoing tracking of glucose levels and biometrics

Future Trajectories in Medical TinyML

  • Approaches involving multi-sensor fusion
  • Tailored health analytics
  • Next-generation low-power AI processors

Conclusion and Path Forward

Requirements

  • A solid grasp of fundamental machine learning principles
  • Hands-on experience with embedded or biomedical hardware
  • Proficiency in development using Python or C

Target Audience

  • Medical professionals
  • Biomedical engineers
  • AI software developers

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