tinyML device monitors packages for damage in transit
tinyML device monitors packages for damage in transit
Arduino Team – September 10, 2022
While the advent of large-scale online shopping has been a great convenience, it has also led to a sharp increase in the number of returned items. This can be attributed to a number of factors, but shipping damage is a big contributor to this problem. Shebin Jose Jacob's solution involves creating a small tracker that accompanies the package on its journey and sends alerts when mishandling is detected.
Jacob started by creating a new Edge Impulse project and collecting about 30 minutes of motion samples from an Arduino Nano 33 BLE Sense's built-in three-axis accelerometer. Each sample was classified into one of five categories ranging from no movement to a hard drop or a vigorous jolt. The features were then generated and used to train a Keras model, which yielded 91.3% accuracy in testing.
More details can be found here in Jacob's project description.
Arduino Team – September 10, 2022
While the advent of large-scale online shopping has been a great convenience, it has also led to a sharp increase in the number of returned items. This can be attributed to a number of factors, but shipping damage is a big contributor to this problem. Shebin Jose Jacob's solution involves creating a small tracker that accompanies the package on its journey and sends alerts when mishandling is detected.
Jacob started by creating a new Edge Impulse project and collecting about 30 minutes of motion samples from an Arduino Nano 33 BLE Sense's built-in three-axis accelerometer. Each sample was classified into one of five categories ranging from no movement to a hard drop or a vigorous jolt. The features were then generated and used to train a Keras model, which yielded 91.3% accuracy in testing.
More details can be found here in Jacob's project description.
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