Edge AI for Healthcare: TinyML for Medical Wearables

Introduction to the Course

Course Objectives

• Grasp the basics of edge AI and TinyML for healthcare wearables.
• Learn how to handle wearable sensor data (ECG/PPG/IMU/temperature) for ML models.
• Acquire practical expertise in feature engineering and designing models for resource-constrained devices.
• Gain mastery in deployment techniques: quantization, latency, memory, and power optimization.
• Delve into anomaly detection and event detection for healthcare wearables.
• Develop the skill to prototype TinyML models for healthcare wearables with real-world constraints.

What Will You Learn (Modules)

Module 1: Model Compression Basics

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

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

Module 2: On-Device Arrhythmia Classification – Case Study

• Understanding ECG signal properties and common arrhythmia types

• Importance of real-time edge inference in cardiology

Module 3: Privacy-Preserving Federated Updates

• Decentralized model training across edge devices

• Comparison with traditional cloud-based learning

Who Should Take This Course?

This course is ideal for:

• Healthcare AI professionals and biomedical engineers building wearable intelligence
• IoT and embedded engineers working on low-power devices
• Data scientists transitioning into wearable sensor analytics and edge AI
• Students in biomedical, electronics, computer science, or data science

Job Opportunities

After completing this course, learners can pursue roles such as:

• Edge AI Engineer (Healthcare)
• TinyML Engineer (Wearables)
• Biomedical Data Scientist (Wearable Analytics)
• Embedded ML Engineer

Why Learn With Nanoschool?

At NanoSchool, we focus on career-relevant learning that builds real capability—not just theory.

• Expert-led training: Learn from instructors with real-world experience in applying skills to industry and research problems.
• Practical & hands-on approach: Build skills through guided activities, templates, and task-based learning you can apply immediately.
• Industry-aligned curriculum: Course content is designed around current tools, workflows, and expectations from employers.
• Portfolio-ready outcomes: Create outputs you can showcase in interviews, academic profiles, proposals, or real work.
• Learner support: Get structured guidance, clear learning paths, and support to stay consistent and finish strong.

Key outcomes of the course

Upon completion, learners will be able to:

• Skills in developing edge AI for healthcare pipelines for wearable biosignals
• Practical skills in TinyML for medical wearables including optimization and deployment thinking
• Confidence in designing anomaly/event detection for real-time health monitoring
• Knowledge of privacy, latency, and battery-aware model trade-offs