
AI-Powered Biosignal Analytics & Remote Patient Monitoring – Hands-on Bootcamp
From raw biosignals to intelligent remote care dashboards.
Skills you will gain:
About Program:
The AI-Powered Biosignal Analytics & Remote Patient Monitoring – Hands-on Bootcamp is a practical, application-focused program designed to bridge the gap between medical wearables, biosignal processing, and real-world digital health solutions. Participants will learn how to work with ECG, PPG, IMU, and SpO₂ data, clean and analyze biosignals, extract meaningful features, build basic machine learning models, and create functional Remote Patient Monitoring (RPM) dashboards.
Aim: To equip participants with practical skills to process biosignals from medical wearables, build AI-driven analytics pipelines, and develop prototype remote patient monitoring (RPM) dashboards for real-time health insights and decision support.
Program Objectives:
- Introduce participants to the fundamentals of medical wearables and biosignal acquisition.
- Provide hands-on experience in cleaning, preprocessing, and analyzing ECG, PPG, IMU, and SpO₂ data.
- Teach practical methods for feature extraction, including heart rate, HRV, and activity metrics.
- Enable participants to build basic machine learning models for health signal classification and prediction.
- Guide learners in developing functional Remote Patient Monitoring (RPM) dashboards with real-time data streams.
- Equip participants with skills to create end-to-end digital health workflows—from sensors to analytics to monitoring.
- Foster understanding of ethics, data privacy, and responsible AI practices in healthcare applications.
What you will learn?
📅 Day 1 – Basics & Health Signal Lab (App – Part 1)
- Intro to medical wearables (ECG, PPG, IMU, SpO₂) & use-cases
- Set up Vibe Code IDE, clone template, run starter app
- Import & clean wearable datasets (file upload, timestamps, basic stats)
- Explore signals (time-series plots, signal explorer)
- Improve signal quality (basic filters, raw vs filtered views)
👉 Outcome: Health Signal Lab v1 – upload, clean, visualize, and filter biosignals.
📅 Day 2 – Features, ML & Health Signal Lab (App – Part 2)
- Heart rate & HRV (peak detection, HR/HRV metrics)
- IMU feature extraction & activity summaries
- Build a simple ML pipeline (features, labels, LogReg/RandomForest)
- Auto “Train Model” + accuracy & confusion matrix in-app
- Generate summary report & mini-project using full pipeline
👉 Outcome: Health Signal Lab v2 – full analytics + basic ML + reporting.
📅 Day 3 – Remote Monitoring & Mini RPM Dashboard
- Intro to Remote Patient Monitoring (RPM) architecture & examples
- Set up Mini RPM Dashboard template in Vibe Code IDE
- Simulate multi-patient HR/SpO₂ streams & data ingestion
- Build live clinician dashboard (patient cards, time-series, auto-refresh)
- Add simple alert rules + discuss ethics, privacy & consent
- Participant demos of both apps
👉 Outcome: Working Mini RPM Dashboard + end-to-end workflow from wearable data → analytics → remote monitoring.
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!

Intended For :
- Healthcare professionals & clinicians interested in medical wearables, digital health, and remote patient monitoring (RPM).
- Biomedical, biotechnology, and healthcare researchers working with physiological signals or digital health data.
- Data scientists, AI/ML engineers, and analysts who want to apply machine learning to ECG, PPG, IMU, SpO₂ and other biosignals.
- Developers and engineers interested in building health data analytics tools, dashboards, and RPM applications.
- Students (UG/PG/PhD) in engineering, life sciences, medicine, or computer science who wish to enter the digital health and health-tech domain.
Prerequisites (recommended, not mandatory):
- Basic familiarity with programming concepts (Python preferred).
- Interest in healthcare, biosignals, or AI/ML applications in medicine.
Career Supporting Skills
Program Outcomes
- Work with real-world biosignal data from medical wearables (ECG, PPG, IMU, SpO₂).
- Clean, visualize, and pre-process time-series health data for analysis.
- Extract key features such as heart rate, HRV, and activity metrics from wearable signals.
- Build and evaluate basic machine learning pipelines for health analytics inside an app workflow.
- Develop a mini Remote Patient Monitoring (RPM) dashboard for multi-patient visualization.
- Implement simple alert rules and discuss ethical, privacy, and consent aspects in digital health.
- Create end-to-end prototypes connecting wearable data → analytics → remote monitoring interfaces.
