AI-Powered Biosignal Analytics & Remote Patient Monitoring – Hands-on Bootcamp

About This Course
The AI-Powered Biosignal Analytics & Remote Patient Monitoring – Hands-on Bootcamp is a practical, hands-on program designed to help you understand and work with medical wearable data and modern digital health systems. This course focuses on real-world applications, allowing you to explore how biosignals are used to monitor and analyze patient health remotely.

Throughout the bootcamp, participants will work directly with real biosignal data such as ECG, PPG, IMU, and SpO₂. You will learn how to clean and process raw signal data, extract meaningful health indicators, build basic machine learning models, and develop functional Remote Patient Monitoring (RPM) dashboards. The goal is to give you practical experience in building complete health analytics workflows—from raw sensor data to actionable health insights.

Aim
To provide participants with hands-on skills to process biosignals from wearable medical devices, build AI-powered analytics pipelines, and create prototype Remote Patient Monitoring (RPM) dashboards that enable real-time health monitoring and decision support.

Course Objectives

• Help participants understand the fundamentals of medical wearables and how biosignals are collected and used
• Provide hands-on experience in cleaning, preprocessing, and analyzing ECG, PPG, IMU, and SpO₂ datasets
• Teach practical techniques for extracting useful health features such as heart rate, HRV, and activity metrics
• Enable participants to build basic machine learning models for biosignal classification and prediction
• Guide learners in developing functional RPM dashboards that simulate real-time health monitoring
• Equip participants with the skills needed to create complete digital health workflows—from data collection to analysis and monitoring
• Develop awareness of ethical considerations, data privacy, and responsible use of AI in healthcare applications

Course Structure

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

• Introduction to medical wearables (ECG, PPG, IMU, SpO₂) and their real-world healthcare applications
• Set up the Vibe Code IDE, clone the provided template, and run the starter application
• Import wearable datasets and perform initial cleaning, including handling timestamps and basic statistics
• Explore biosignals using time-series plots and interactive signal visualization tools
• Improve signal quality using filtering techniques and compare raw and processed signals
  👉 Outcome: Build Health Signal Lab v1 – an application capable of uploading, cleaning, visualizing, and filtering biosignal data

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

• Learn heart rate and HRV analysis using peak detection and signal processing methods
• Perform IMU feature extraction and generate activity summaries from motion data
• Build a simple machine learning pipeline using features, labels, and models such as Logistic Regression or Random Forest
• Train models directly within the application and evaluate performance using accuracy metrics and confusion matrices
• Generate summary reports and complete a mini-project using the full analytics pipeline
  👉 Outcome: Build Health Signal Lab v2 – a complete biosignal analytics system with feature extraction, ML modeling, and reporting capabilities

✅ Module 3 – Remote Monitoring & Mini RPM Dashboard

• Understand the architecture and workflow of Remote Patient Monitoring (RPM) systems
• Set up a Mini RPM Dashboard template using the Vibe Code IDE
• Simulate multi-patient heart rate and SpO₂ data streams and implement data ingestion
• Build a live clinician dashboard with patient summaries, time-series visualizations, and automatic updates
• Add simple alert rules and understand the importance of ethics, privacy, and patient consent
• Present and demonstrate the applications developed during the bootcamp
  👉 Outcome: Create a fully functional Mini RPM Dashboard and complete an end-to-end workflow from wearable data → analytics → remote monitoring

Who Should Enrol?

• Healthcare professionals and clinicians interested in digital health and wearable monitoring technologies
• Biomedical, biotechnology, and healthcare researchers working with physiological or biosignal data
• Data scientists, AI/ML engineers, and analysts interested in applying machine learning to biosignals
• Software developers and engineers building healthcare analytics tools or monitoring systems
• Students (UG/PG/PhD) in engineering, life sciences, medicine, or computer science who want to enter the health-tech field

Prerequisites (recommended, not mandatory):

• Basic understanding of programming concepts (Python is preferred but not required)
• Interest in healthcare technology, biosignal analysis, or AI/ML applications in medicine