Artificial intelligence revolutionizes the precision of human activity recognition, and edge AI enables these algorithms to be embedded everywhere. They can run locally without disclosing any personal information and are embedded in devices such as smartwatches and wristbands.
If you are developing such an application and are concerned about power consumption, this use case is for you. The gym activity recognition use case serves as a fitness example for wearable devices, recognizing activities like bicep curls, lateral raises and squats - all with very low power consumption, thanks to the Machine Learning Core (MLC) available in ST MEMS sensors.
The accelerometer is configured with 8 g full scale, 30 Hz output data rate, low-power mode 1.
The sensor orientation for this algorithm is east-north-up (ENU):
To implement this algorithm with a decision tree, all the data logs have been acquired using the device (LSM6DSV16X) mounted on a wristband on the left hand (or right hand).
Power consumption (sensor + algorithm): 16.5 uA
The decision tree has around 30 nodes and it is configured to detect the different classes.
The output of the decision tree classifier is stored in the register MLC1_SRC (70h):
The configuration generates an interrupt (pulsed and active high) on the INT1 pin every time the register MLC1_SRC (70h) is updated with a new value.
The duration of the interrupt pulse is 33.3 ms in this configuration.
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.