There is a huge need for inexpensive, easily deployable solutions for COVID-19 and other flu-related early detection. Together with the UN, Hackster, Edge Impulse, and many others we recently launched the UN COVID Detect & Protect Challenge aiming to create easily deployable solutions for the prevention and detection of flu in developing countries. In this tutorial, we show how to 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. This project and the dataset were originally started by Kartik Thakore to help in the COVID-19 effort.
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