TinyML in Healthcare: AI on Wearable Devices Training Course
TinyML represents the embedding of machine learning capabilities into low-power, resource-constrained wearable and medical devices.
This instructor-led live training, available online or onsite, is designed for intermediate-level practitioners seeking to implement TinyML solutions for healthcare monitoring and diagnostic applications.
Upon completing this training, participants will be equipped to:
- Design and deploy TinyML models for real-time processing of health data.
- Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
- Optimize models for wearables with limited power and memory resources.
- Evaluate the clinical relevance, reliability, and safety of outputs generated by TinyML.
Course Format
- Lectures complemented by live demonstrations and interactive discussions.
- Practical exercises involving wearable device data and TinyML frameworks.
- Guided implementation activities within a controlled lab environment.
Course Customization Options
- To tailor the training to specific healthcare devices or regulatory workflows, please contact us to customize the program.
Course Outline
Foundations of TinyML in Healthcare
- Key characteristics of TinyML systems
- Specific constraints and requirements within the healthcare sector
- Overview of wearable AI architectures
Biosignal Acquisition and Preprocessing
- Utilizing physiological sensors
- Methods for noise reduction and signal filtering
- Extracting features from medical time-series data
Developing TinyML Models for Wearables
- Selecting appropriate algorithms for physiological data
- Training models within resource-constrained environments
- Assessing performance using health-related datasets
Deploying Models on Wearable Devices
- Leveraging TensorFlow Lite Micro for on-device inference
- Integrating AI models into medical wearables
- Conducting testing and validation on embedded hardware
Power and Memory Optimization
- Strategies for minimizing computational load
- Optimizing data flow and memory utilization
- Striking a balance between accuracy and efficiency
Safety, Reliability, and Compliance
- 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 systems for cardiac monitoring
- Activity recognition in rehabilitation settings
- Continuous tracking of glucose levels and biometrics
Future Directions in Medical TinyML
- Approaches to multi-sensor fusion
- Personalized health analytics
- Next-generation low-power AI chips
Summary and Next Steps
Requirements
- A solid grasp of fundamental 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
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
TinyML in Healthcare: AI on Wearable Devices Training Course - Enquiry
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