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 using ST MEMS sensors.
We combined two advanced features available in the ST MEMS sensors: the machine learning core (MLC) and the finite state machine (FSM):
Power consumption (sensor + algorithm): 14.7 uA
The output of the MLC can be read from the MLC1_SRC (34h) register:
The FSM detects the following states:
The configuration generates an interrupt (pulsed and active high) on the INT1 pin every time the register MLC1_SRC (34h) is updated with a new value (when the state detected by the MLC changes). The duration of the interrupt pulse is 40 ms in this configuration.
The configuration generates an interrupt (pulsed and active high) on the INT2 pin when either a free-fall or an impact event is detected by the FSM. The free-fall interrupt remains active as long as the package is airborne. The FSM_STATUS (13h) register allows determining which FSM has generated the interrupt in order to distinguish between impact and free-fall events.
A complete software solution for desktops to enable AI features on smart sensors. It allows users to analyze data, evaluate embedded libraries, and design no-code algorithms for the entire portfolio of MEMS sensors.
Smart sensors capable of directly processing the data they capture and delivering meaningful insights to the host device. By processing data locally, smart sensors reduce transmitted data and cloud processing requirements, thus lowering power consumption at the system level.