New Year Offer End Date: 30th April 2024
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Program

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

INR 1999 /- OR USD 50

Get an e-Certificate of Participation!

2024Certfiacte

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.