Although the widespread use of 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’s solution José Jacob consists of building a small tracer that accompanies the package throughout its journey and sends alerts when mishandling is detected.
Jacob started with create a new Edge Impulse project and collecting approximately 30 minutes of motion samples from a Sense Arduino Nano 33 BLEof the onboard 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.
To communicate with the outside world, Jacob added a GSM module that allows the Nano 33 BLE Sense to send alerts over a 3G network to a standby Firebase endpoint. When the database is updated, the new data is propagated to a user web page that displays the current status of the package as well as any important events.
More details can be found here in writing Jacob’s project.