Hands-on Medical Wearable Data Lab: From Biosignals to Remote Monitoring App

About This Course
The “Hands-on Medical Wearable Data Lab” offers participants a practical, immersive experience in collecting, analyzing, and interpreting biosignals from wearable medical devices. In this course, participants will learn how to build remote health monitoring applications, explore real-time data processing, and advance wearable technology for better healthcare solutions.

Aim
The aim of the “Hands-on Medical Wearable Data Lab” is to equip participants with the skills needed to develop remote patient monitoring (RPM) systems. The course focuses on empowering learners to collect and analyze biosignals like ECG, PPG, IMU, and SpO₂, helping them build applications that support real-time health tracking and improve personalized healthcare delivery through wearable technology.

Course Objectives

• Fundamentals of Medical Wearables: Gain knowledge of wearable medical devices and the biosignals they collect, including ECG, PPG, IMU, and SpO₂.
• Biosignal Data Processing: Learn to process, clean, and analyze wearable data to extract meaningful insights for health monitoring.
• Signal Exploration & Feature Extraction: Master the techniques for signal exploration, feature extraction, and application development using biosignals.
• Integration with Machine Learning: Understand how to integrate machine learning models for tracking heart rate variability, activity monitoring, and predictive health analytics.
• Remote Monitoring Applications: Build practical skills in designing and developing remote patient monitoring (RPM) dashboards and applications.
• Real-Time Data Systems: Learn to develop real-time data ingestion systems and clinician-focused user interfaces for seamless health monitoring.
• Ethical Considerations: Understand the ethical implications and privacy concerns involved in wearable health technology.

Course Structure

📅 Module 1 – Basics & Health Signal Lab (App – Part 1)

• Introduction to Medical Wearables: Explore ECG, PPG, IMU, and SpO₂ devices and their use-cases in healthcare.
• Setting Up Development Environment: Set up Vibe Code IDE, clone the template, and run a starter app.
• Data Import & Cleaning: Learn how to import and clean wearable datasets, handle timestamps, and calculate basic statistics.
• Signal Exploration: Visualize biosignals through time-series plots and signal explorers.
• Signal Quality Improvement: Apply basic filters and compare raw vs. filtered views of biosignals.
• Outcome: Health Signal Lab v1 – successfully upload, clean, visualize, and filter biosignals.

📅 Module 2 – Features, ML & Health Signal Lab (App – Part 2)

• Heart Rate & HRV Analysis: Understand peak detection and metrics for heart rate and heart rate variability (HRV).
• IMU Feature Extraction: Extract and analyze features from IMU data for activity monitoring.
• Building a Simple ML Pipeline: Develop a basic ML pipeline with features, labels, and models such as Logistic Regression and Random Forest.
• Model Training & Evaluation: Auto-train models, analyze accuracy, and visualize confusion matrices in-app.
• Project Development: Generate a summary report and complete a mini-project using the full pipeline.
• Outcome: Health Signal Lab v2 – complete analytics, machine learning, and reporting capabilities.

📅 Module 3 – Remote Monitoring & Mini RPM Dashboard

• Introduction to RPM Architecture: Explore the architecture and real-world examples of remote patient monitoring (RPM) systems.
• Building the Mini RPM Dashboard: Set up the RPM Dashboard template in Vibe Code IDE, simulate multi-patient HR/SpO₂ data streams, and create a data ingestion system.
• Clinician Dashboard Development: Design a clinician dashboard with time-series visualization, patient cards, and auto-refresh features.
• Alerts & Ethics: Add basic alert rules to the dashboard and discuss ethical considerations like privacy, consent, and data security.
• Participant Demos: Showcase the completed apps and their functionalities.
• Outcome: A fully working Mini RPM Dashboard, with an end-to-end workflow from wearable data collection to analytics and remote monitoring.

Who Should Enrol?

• Healthcare Professionals: Clinicians, healthcare practitioners, and researchers interested in wearable devices and remote patient monitoring.
• Data Scientists & Engineers: Individuals working with biosignal data and machine learning in healthcare.
• App Developers & Students: Developers and students interested in building applications for health data analysis and real-time monitoring.
• Anyone Interested in Wearables: Individuals keen on learning about medical wearables, signal processing, and real-time health analytics.

Prerequisites (recommended, not mandatory):

• Basic familiarity with programming concepts (Python preferred)
• Interest in healthcare, biosignals, or AI/ML applications in medicine