TinyML in Healthcare Training in Auckland, New Zealand

This training program introduces participants to TinyML and its applications in healthcare monitoring, wearable devices, and edge-based health data processing. Participants will explore physiological sensor data, signal processing, lightweight machine-learning models, model optimization, and deployment concepts for resource-constrained devices. The program also covers healthcare data privacy, safety, reliability, and regulatory considerations.

Healthcare Data Processing and TinyML Model Development

Introduction to TinyML in Healthcare

• TinyML fundamentals and healthcare applications

• On-device AI versus cloud-based healthcare AI

• Wearable health monitoring and remote monitoring use cases

• Benefits, limitations, and challenges of healthcare TinyML

Healthcare Sensors and Data Processing

• Physiological signals, including ECG, PPG, SpO2, temperature, and motion

• Wearable devices and healthcare data sources

• Data quality, noise, and reliability considerations

• Data preprocessing, segmentation, and feature extraction

Lightweight Machine-Learning Models

• Selecting suitable models for healthcare applications

• Classical machine learning versus lightweight neural networks

• Training models using sample healthcare datasets

• Handling imbalanced data and avoiding overfitting

Model Evaluation for Healthcare Applications

• Training, validation, and testing strategies

• Sensitivity, specificity, precision, and recall

• Understanding false positives and false negatives

• Evaluating model limitations and reliability

Hands-on exercises

Model Optimization, Deployment, and Responsible Healthcare AI

TinyML Model Optimization

• Model quantization and compression concepts

• Memory footprint and inference latency optimization

• Power efficiency and resource constraints

• Balancing model performance with device capabilities

TinyML Deployment for Healthcare Devices

• End-to-end TinyML deployment workflow

• Introduction to TensorFlow Lite for Microcontrollers

• On-device inference and embedded resource management

• Firmware integration and device testing concepts

Privacy, Safety, and Regulatory Considerations

• Privacy-by-design for healthcare data

• On-device processing and minimizing cloud data transmission

• Fail-safe design and human oversight

• Healthcare AI validation and medical-device regulatory concepts

Edge-to-Cloud Integration in Healthcare

• Edge and cloud roles in healthcare monitoring

• Secure data synchronization and remote monitoring concepts

• Alerting and escalation workflows

• Model monitoring and continuous improvement principles

Hands-on exercises