Capstone Project
| Group | 2024-14 | Status | inprogress |
| Title | RespiRhythm: Digital Stethoscope with Real Time Sound Analysis for Medical Training | ||
| Supervisor | Dr. M. Kahrizi and Dr. D. Qiu | ||
| Description | This project aims to develop a digital stethoscope specifically designed for medical
and nursing students, integrating advanced technology to enhance traditional
diagnostic techniques. With the help of a high fidelity microphone this device will
help students distinguish and analyze heart, lung, and vascular sounds more
effectively, which are then wirelessly transmitted to a custom-built Android app for
further analysis.
The device will feature an OLED screen displaying real-time visualizations, which will later be linked to an android app which includes detailed graphs and phonocardiograms, enabling students to identify and understand various heart murmurs and respiratory conditions. Additionally, key features such as the ability to log and retrieve patient history for ongoing educational use, AI-driven murmur detection, and adult and pediatric modes will be included. The app will also offer 3D visualizations of sound patterns, allowing students to gain deeper understanding into underlying medical conditions, making it a comprehensive learning tool. To validate the quality of our stethoscope to diagnose heart, lung, and vascular conditions, we will work alongside professionals in healthcare and technology to ensure accuracy of quality of captured sounds.In order to evaluate the device usability and instructional value, we will also test it on medical students. Specifications like sensitivity and sound clarity will be compared to industry standard stethoscopes like Littmann. The team will use clinical sound libraries & databases like Physionet to test the accuracy of the gadget even more. We will assess the accuracy of our AI murmur detection system by comparing its output with known medical outcomes. |
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| Student Requirement | Background in programming microcontrollers (e.g., ESP32). Experience in signal processing Experience in android development, User Interface Design, SQL Database Experience working with low energy Bluetooth modules Background in Integrating AI in app development | ||
| Tools | Test Equipment: Oscilloscope Soldering station Multimeter Medical Sounds Database Clinical Study Simulation Software Android App Developer: Android Studio Firmware for Microcontroller: Arduino IDE PCB Design Software CAD Software Signal Processing Software: Python, MATLAB Hardware: High Fidelity microphone (EX: MEMS microphone) Microcontroller (ex:ESP32) Bluetooth module OLED screen Analog to Digital signal converter Power source 3D printing parts for potential designs Diaphragm Magnetic Attachment Mechanism High Quality acoustic tubing [Steel or Aluminum Alloy] Comfortable earpiece design/ear tips [Silicon or Rubber] | ||
| Number of Students | 5-6 | ||
| Students | Sakanah Dharmalingam, Christian Dingayan, Krupesh Patel, Sumit Patel, Anjanaa Poobalasingam, Madurra Satchithanantham | ||
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