We all strive to maintain healthier lifestyles, yet the kitchen is often the most challenging environment by far due to it containing a wide range of foods and beverages. The Smart-Badge project, created by a team of researchers from the German Research Centre for Artificial Intelligence (DFKI), aims to track just how many times we reach for the refrigerator door or drink water using machine learning and a suite of environmental sensors.

The wearable device itself is comprised of a single PCB that houses a pair of microcontrollers, an NXP iMXRT1062 for quickly gathering complex data, and an Arduino Nano 33 BLE Sense for collecting more basic samples. Whether it’s the digital gas sensor, the accelerometer, an IR thermal array, or an air pressure sensor, each reading is compiled into a single stream which updates at 6Hz and can either be stored locally on an SD card or sent via Bluetooth® to a phone.

After having 10 volunteers perform various tasks around a mock kitchen while wearing the Smart-Badge and then labeling each activity, the researchers were able to collect a sizable dataset. The 791 total data channels were fed through several layers of a neural network that could ultimately classify activities with 92.4% accuracy.

For more details on the project, you can read the team’s paper here.

Image credit: Liu and Suh et al.

Boards:Nano 33 BLE Sense

Categories:Arduino

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