Detecting the “let” in a table tennis game

Did the ball hit the net? For all the ping pong games with no official referee, we developed a smart sensor that can detect when the ball hits the table or the net using machine learning! This application is easily transferable to other use cases using NanoEdge AI Studio.


– Use of an accelerometer to collect the vibration behavior of the net
– Define a collection of classes (3 classes of data: let, shock table, normal set)
– Log and import these data in the NEAI Studio tool and generate the corresponding library
– Test the library on the STEVAL-PROTEUS1 board


Accelerometer: ISM330DHCX


3 classes of data: Let, table choc, normal play
Length data: 64 * 3 axis
Data rate: 416 Hz; Range: 2g


2 classes (let & normal play):
100 % accuracy, 0.8 KB RAM, 0.2 KB Flash
3 classes:
95 % accuracy, 0.8 KB RAM, 0.5 KB Flash

A green point means we are able to correctly predict if the finish will pass a visual inspection or not.
A red point means we were incorrect.