TinyML in Healthcare: AI on Wearable Devices Training Course
TinyML refers to the application of machine learning within the constraints of low-power, resource-limited wearable and medical devices.
This instructor-led live training, available online or onsite, is designed for intermediate-level professionals looking to implement TinyML solutions for healthcare monitoring and diagnostic purposes.
Upon completion, participants will be equipped to:
- Design and deploy TinyML models capable of processing real-time health data.
- Collect, preprocess, and interpret biosensor data to generate AI-driven insights.
- Optimize models for wearable devices that operate under strict power and memory constraints.
- Assess the clinical relevance, reliability, and safety of outputs generated by TinyML systems.
Course Format
- Interactive lectures supported by live demonstrations and open discussions.
- Practical exercises involving wearable device data and TinyML frameworks.
- Guided implementation tasks within a controlled lab environment.
Customization Options
- To tailor the training to specific healthcare devices or regulatory workflows, please reach out to us to customize the program.
Course Outline
Foundations of TinyML in Healthcare
- Key characteristics of TinyML systems
- Specific constraints and requirements in healthcare settings
- An overview of wearable AI architectures
Biosignal Acquisition and Preprocessing
- Interfacing with physiological sensors
- Techniques for noise reduction and signal filtering
- Extracting meaningful features from medical time-series data
Developing TinyML Models for Wearables
- Selecting appropriate algorithms for physiological data
- Training models within constrained environments
- Performance evaluation on health-related datasets
Deploying Models on Wearable Devices
- Leveraging TensorFlow Lite Micro for on-device inference
- Integrating AI models into medical wearables
- Testing and validating on embedded hardware
Power and Memory Optimization
- Methods for minimizing computational load
- Optimizing data flow and memory utilization
- Achieving a balance between model accuracy and efficiency
Safety, Reliability, and Compliance
- Regulatory considerations for AI-enabled wearables
- Ensuring 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 involving 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
- Hands-on experience with embedded systems or biomedical devices
- Proficiency in Python or C-based development
Target Audience
- Healthcare practitioners
- 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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