Have you ever wondered about the status of the package you have delivered? Has any impact caused significant damage to the contents of the package?

Sensors can assist in monitoring these conditions, but it is essential to have low power consumption to increase battery life. This is where edge AI comes into play. At the sensor level, it can help save power while tracking the state of your package and detecting any possible events.

In this use case, we will show you how to implement a smart asset tracking solution usingST MEMS sensors.

Approach

We combined two advanced features available in the ST MEMS sensors: the machine learning core (MLC) and the finite state machine (FSM):

• We used MEMS-Studio to generate and configure a decision tree model with three nodes to detect the different classes.
• The MLC processes the accelerometer data to detect the state of the package (e.g. stationary, in motion, shaken).
• The FSM processes raw and filtered accelerometer data to detect impacts and falls.
• In this example, the threshold for impact detection is set to 0.5 g, and the angle for upright is set to 26.
• The interrupts generated on the INT1/INT2 pins of the sensor allow to wake up the microcontroller only when the desired events have been detected. 

Sensor

Data

Results