• Vishal Shrivastav
• Anas Shaikh
• Mayur Chaudhary
• Mohsin Baig
• Prof. R S Deshmukh

There is a huge need for inexpensive, easily deployable solutions for COVID-19 and other flu-related early detection. In this project, we use Edge Impulse Machine learning on an Arduino Nano BLE Sense to detect the presence of coughing in real-time audio. We built a dataset of coughing and background noise samples, and applied a highly optimized TinyML model, to build a cough detection system that runs in real-time in under 20 KB of RAM on the Nano BLE Sense. This same approach applies to many other embedded audio pattern matching applications, for example, elderly care, safety, and machine monitoring.

Arduino Nano BLE Sense, TinyML, Edge Impulse

"Cough Detection using Arduino and ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.c211-c214, May-2021, Available :http://www.jetir.org/papers/JETIR2105281.pdf

"Cough Detection using Arduino and ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppc211-c214, May-2021, Available at : http://www.jetir.org/papers/JETIR2105281.pdf