CoughNet: Low-cost sensors count coughs in a room while keeping identities private

When the COVID-19 pandemic swept across the world, it exposed a glaring gap in public health monitoring: hospitals, waiting rooms, and other crowded indoor spaces had no practical way to gauge how many people around them were coughing, and therefore potentially spreading a respiratory illness. Researchers at Binghamton University believe they have found an answer, and it comes in the form of a small, inexpensive network of listening devices that can count coughs in a room without ever identifying the people making them. The prototype, called CoughNet, was developed by Assistant Professor Dali Ismail of the Thomas J. Watson College of Engineering and Applied Science, together with PhD students Amir Esmaeili and Maryam Fazli, and was presented at the 2026 IEEE/ACM Conference on Connected Health: Applications, Systems, and Engineering Technologies (CHASE) in August 2026.

At its core, CoughNet is a distributed acoustic sensing system built around three Raspberry Pi single-board computers, each fitted with a built-in microphone and positioned at different points in a room. Raspberry Pis are small, general-purpose computers that typically cost less than fifty dollars per unit, which makes the hardware footprint of the system remarkably modest. When someone coughs, all three microphones pick up the sound, but the device closest to the source registers it at the highest amplitude. That nearest unit acts as what the researchers describe as a reference microphone, capturing a short recording of the cough and running the audio file through an artificial-intelligence model trained to determine whether the sound is an authentic cough rather than a door slam, a dropped object, or a burst of speech.

The clever part of the design lies in how the system decides whether a cough comes from someone it has already counted. Rather than attempting any form of speaker identification, CoughNet exploits basic physics: sound travels at a known speed through air, so a cough originating from a particular spot in the room will arrive at each of the three microphones with a characteristic combination of loudness and arrival delay. The system performs cross-correlation between the reference recording and the signals captured by the two more distant units, comparing how strong the cough was in each recording and how long the sound took to reach each device. If the acoustic fingerprint matches, the system concludes that the same individual produced the cough; if the timing and amplitude profile differ, it registers a new, unique cougher.

Over a series of coughs, this approach allows CoughNet to build a statistical picture of the room. Multiple coughs that share a similar loudness and time-of-arrival pattern are grouped together as likely coming from the same person, while divergent patterns are flagged as additional individuals. The end result is a running count of unique coughing people in a defined space, which is precisely the kind of aggregate signal that could alert medical staff to a brewing respiratory outbreak without revealing anything about who those individuals are. Ismail emphasized that the system determines only that a cough occurred, never that a specific person coughed.

The emphasis on lightweight, efficient computation is what sets CoughNet apart from much of the existing work in disease monitoring. As Ismail noted, most public health and disease monitoring research has been machine-learning-heavy and processing-intensive, typically demanding servers or cloud infrastructure to run. His team deliberately set out to treat cough detection and cougher counting as a lightweight application that could eventually run on practical, low-cost devices. That constraint shaped every layer of the system, from the choice of hardware to the wireless protocol used to move data between nodes.

That wireless protocol is long-range, low-power radio technology known as LoRa, and it is central to CoughNet’s energy story. LoRa covers far more indoor space than a single Wi-Fi access point while consuming so little power that sensors can run for years on batteries. Ismail estimated that a typical lower-tier sensor in the system can operate on AA batteries for roughly ten years. The trade-off is that LoRa can only carry small messages, so the units cannot simply stream raw audio to each other or to a central hub. Instead, the system is selective about what it transmits: if a cough recording is clean, the sensor processes and groups it locally on the device; if the recording is noisy or unclear, the unit offloads only a small clip to a nearby, more powerful computer for further analysis. In Ismail’s words, it is a combination of networking and computation, with both optimized to work efficiently on tiny devices.

Privacy considerations shaped the design just as much as engineering efficiency did. Many public health sensing systems deployed in shared spaces rely on thermal cameras or visual sensors, which can capture far more information about individuals than their operators need. CoughNet reacts only to short bursts of sound that resemble a cough. There is no voice recognition, no speech-to-text processing, and no attempt to link any acoustic event to a person’s identity. Moreover, the system processes only enough audio to run its detection algorithm and then immediately deletes the raw sound once the analysis is complete. Ismail argued that if the same level of accuracy can be achieved without a camera or additional sensors, the privacy-sensitive job is done, especially in indoor environments such as hospitals where surveillance concerns are acute.

The prototype is very much a proof of concept, but the research team has a clear roadmap for extending it. The current version listens to audio and classifies it as a cough; the next major goal is to analyze coughs for additional context by examining their specific acoustic signatures. Different respiratory conditions can produce subtly different cough sounds, and the researchers want to explore whether those features can be used to estimate the probability that a coughing individual has a particular illness, such as a flu-like infection, COVID-19, or bronchitis. If that line of research succeeds, CoughNet could evolve from a simple counting tool into an early-warning system capable of hinting at the nature of an outbreak, not merely its existence.

The most obvious application is in medical settings: installing the technology in hospital waiting rooms or clinical units so that staff receive an alert when the rate of coughing in a defined area spikes, allowing them to respond with better information and a more efficient allocation of resources such as isolation rooms, testing kits, and protective equipment. But Ismail also sees direct-to-consumer possibilities. He wants the technology to eventually run on active noise-cancelling headphones and other wearable devices, turning everyday gadgets into background health sentinels. In theory, such a device could quietly warn its wearer that they are entering an area where the risk of respiratory illness appears elevated, giving people a chance to take precautions before exposure occurs.

For the researchers, the breadth of possible directions is part of the appeal. Ismail acknowledged that this is his team’s first work in the health domain, but he expressed enthusiasm for the road ahead, noting that work of this kind has a tangible impact on society. If CoughNet and systems like it mature, the humble cough, long treated as background noise in public spaces, could become a measurable, privacy-respecting signal that helps health systems detect and contain the next respiratory outbreak before it spreads.

Subject of Research: A lightweight acoustic sensor network for privacy-preserving detection and counting of multiple coughers in indoor spaces

Article Title: New sensor system tracks coughs in a room — without knowing who's coughing

Article References: New sensor system tracks coughs in a room — without knowing who's coughing. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: CoughNet, Binghamton University, cough detection, Raspberry Pi, LoRa, edge computing, privacy, respiratory illness, disease surveillance, acoustic sensing, machine learning, public health