Get in Touch
 Duration 21 hours

Course Outline

Core Principles of TinyML in Healthcare

  • Key features of TinyML systems
  • Specific constraints and needs in the healthcare sector
  • Introduction to AI architectures for wearables

Acquisition and Preprocessing of Biosignals

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

Building TinyML Models for Wearables

  • Choosing suitable algorithms for physiological data
  • Training models within constrained environments
  • Assessing model performance on health-related datasets

Model Deployment on Wearable Hardware

  • Implementing on-device inference with TensorFlow Lite Micro
  • Integrating AI models into medical wearables
  • Conducting testing and validation on embedded hardware

Optimizing Power and Memory Usage

  • Strategies to minimize computational load
  • Enhancing data flow and memory management
  • Achieving a balance between accuracy and efficiency

Safety, Reliability, and Regulatory Compliance

  • Regulatory aspects of AI-enabled wearables
  • Guaranteeing robustness and clinical practicality
  • Implementing fail-safe mechanisms and error management

Case Studies and Medical Applications

  • Systems for wearable cardiac monitoring
  • Activity recognition in rehabilitation contexts
  • Continuous tracking of glucose levels and biometrics

Future Trends in Medical TinyML

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

Conclusion and Recommended Next Steps

Requirements

  • Foundational knowledge of machine learning principles
  • Practical experience with embedded or biomedical devices
  • Proficiency in Python or C-based development

Target Audience

  • Clinicians and healthcare specialists
  • Biomedical engineers
  • AI developers

Number of participants


Price per participant

Upcoming Courses

Related Categories