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

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.

• 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.

• 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.

• 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.