Problem / Need Statement (problem-first)

There is no affordable, user-friendly, and reliable method for patients to identify potential cardio-pulmonary issues without the presence of medical professionals, as well as capture and share high-quality cardio-pulmonary sound data outside of clinical settings. This creates a critical gap in early detection, chronic condition management, and telehealth diagnostics.

In remote healthcare and in resource-limited settings, clinicians cannot reliably assess cardio-pulmonary health because patients lack access to proper diagnostic tools at home.

• Telehealth limitation: During virtual visits, doctors can only observe and ask questions — they cannot auscultate the heart and lungs, which is a critical first step in detecting arrhythmias, murmurs, asthma, COPD, or pneumonia.
• Chronic-care gap: Patients with chronic heart or lung disease are expected to self-monitor using scales, pulse oximeters, or blood pressure cuffs, but there is no widely accessible way to monitor and track their cardio-pulmonary sounds.
• Impact: This leads to late detection of deterioration, unnecessary ER visits, and increased costs. Existing professional-grade digital stethoscopes are expensive, designed for trained clinicians, and not accessible to lay users at home.

There is a critical need for a portable, intelligent stethoscope that can analyze auscultation data (body sounds) in real time and distinguish between lung and heart conditions using machine learning, aiding in early detection, triage, and remote diagnosis.

Project Objective

Design and develop a smart stethoscope system that:

• Records heart and lung sounds using high-fidelity sensors.
• Processes the sound signals to extract meaningful features.
• Uses machine learning algorithms (trained on labeled auscultation data) to:
  • To determine the type of waveform as cardiac or pulmonary.
  • Detect specific conditions (e.g., murmur, wheezing, crackles).
• Displays the results on a connected mobile or embedded interface, allowing the option to record and send critical data

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