Edge AI for Healthcare: TinyML for Medical Wearables

About This Course

In healthcare, wearable devices are becoming smarter, smaller, and more powerful. With Edge AI and TinyML, we can build on-device, low-latency, and energy-efficient models that can process data in real-time without relying on the cloud. This has profound implications for creating privacy-preserving, affordable, and accessible healthcare solutions.

This technical, hands-on course dives into TinyML and Edge AI for medical wearables, helping you develop AI-driven health monitoring systems that operate directly on resource-constrained devices like Arduino Nano BLE Sense, Raspberry Pi Pico, and Edge Impulse. From heart rate anomaly detection to fall prediction, this course will empower you to create life-saving solutions with cutting-edge machine learning technologies.

Aim

To provide healthcare innovators, engineers, and developers with the tools, frameworks, and hands-on experience needed to design and deploy AI-powered medical wearables using TinyML and Edge AI, enabling real-time, efficient, and privacy-preserving healthcare monitoring solutions.

Course Objectives

By the end of this course, participants will be able to:

• Understand the potential of TinyML for the next generation of medical devices

• Build real-time AI applications for healthcare wearables using Edge AI

• Prototype privacy-preserving AI solutions for healthcare monitoring

• Gain proficiency in developing low-latency, power-efficient wearable devices

• Foster interdisciplinary skills across AI, embedded systems, and healthcare

• Lead the adoption of decentralized health monitoring technologies for improved patient care

Course Structure

Module 1: Model Compression Basics

• Why Model Compression Matters:

  • Challenges of deploying deep learning models on resource-constrained edge devices

  • Trade-offs: Accuracy vs. Efficiency in healthcare applications

• Compression Techniques Overview:

  • Pruning (structured/unstructured)

  • Quantization (8-bit, binary networks)

  • Knowledge Distillation basics

• Hands-on Compression Lab:

  • Applying pruning and quantization using TensorFlow Lite or PyTorch Mobile

  • Benchmarking model latency and size

Module 2: On-Device Arrhythmia Classification – Case Study

• Introduction to ECG & Arrhythmia Detection:

  • Understanding ECG signal properties and common arrhythmia types

  • Importance of real-time edge inference in cardiology

• End-to-End Model Workflow:

  • Data preprocessing and labeling (using the MIT-BIH dataset)

  • Model selection: CNN, LSTM for time-series classification

• Deployment & Evaluation:

  • Deploying models on microcontrollers (Arduino, EdgeTPU)

  • Evaluating performance metrics: accuracy, latency, power usage

Module 3: Privacy-Preserving Federated Updates

• Fundamentals of Federated Learning:

  • Decentralized model training across edge devices

  • Comparison with traditional cloud-based learning

• Ensuring Data Privacy:

  • Introduction to differential privacy in federated updates

  • Homomorphic encryption & secure aggregation techniques

• Use Case Simulation:

  • Implementing a federated training round using PySyft or Flower

  • Simulating data from multiple hospitals for arrhythmia classification

Who Should Enrol?

• Students & Researchers in AI, biomedical engineering, or healthcare technologies

• Ph.D. Scholars working on TinyML, federated learning, or wearable health monitoring

• Faculty & Academicians teaching AI, embedded systems, or digital health

• MedTech Developers & Engineers building real-time, low-power medical AI devices

• AI/ML Professionals interested in model compression and on-device inference

• Healthcare Innovators focused on privacy-preserving, intelligent wearable solutions