🎧 Listen to Your Lungs
- DetectED
- Jan 28
- 2 min read
Updated: Mar 31
What if a simple device could listen for early signs of illness — before someone realizes they’re sick?
In this STEM Club presentation, students explore how sound, data, and engineering can be used to monitor respiratory health. Using a Micro:bit and its built-in microphone, students build a sensor that detects coughs and breathing patterns — inspired by real technologies used in smart inhalers, sleep monitors, and remote patient care.
Why This Project Matters
Respiratory illnesses spike in winter and disproportionately affect vulnerable communities.
This project challenges students to think about:
How early detection can prevent outbreaks
How sound can become health data
How low-cost tools could support schools, shelters, and remote clinics
Students aren’t just coding — they’re designing public health tools.
The Science Behind It
Sound is a physical signal.
Microphones convert sound waves into electrical signals. By measuring volume levels and setting thresholds, a system can detect events like:
Coughs
Wheezes
Deep or labored breathing
This is the foundation of audio-based health technology, used in cough counters, asthma trackers, and sleep apnea monitors.
What Students Build
In this presentation, students:
Program a Micro:bit to monitor sound levels in real time
Calibrate a baseline for normal room noise
Set thresholds to detect cough-level events
Log and visualize respiratory events on the device
Send alerts wirelessly to simulate a nurse’s monitoring station
Students see how raw sound becomes actionable health data.
Interactive Simulation: Outbreak Detection
Students are placed into a real-world scenario:
You’re a health tech team in a remote clinic. A respiratory illness may be spreading. Can you detect it in time?
Teams take on different roles:
Patient simulator: generates cough sounds
Sensor team: monitors data and counts events
Response team: decides whether to monitor, isolate, or escalate
The goal: detect the outbreak before time runs out.
Health Equity & Ethics
Students are asked to think deeper:
Why are medical-grade monitors often inaccessible?
How can we monitor health without invading privacy?
Should sound-based monitoring record audio — or just patterns?
The discussion shifts from can we build this? to should we — and how?
Where This Can Go Next
This project can evolve into:
Long-term cough monitoring studies
AI models that classify cough vs. laugh
Non-audio respiratory sensing systems
Science fair or independent research projects
This is how early ideas turn into real health innovation.
Explore the Full Presentation
The attachments include:
📊 The slide deck
🧾 Printable activity materials
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