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Gesture recognition for gaming 

Minority report is not fiction anymore. Either for a better user experience or for pandemic precautionary measures gesture-based control can bring benefits. For demonstration purposes we have created 4 classes to distinguish basic gestures, but the model can be trained with any gestures providing a wide range of new features to the final user. NanoEdge AI Studio support the Time-of-Flight sensor, but this application can be addressed with other sensor such as radar and more. 


  • We are using a Time-of-Flight sensor rather than a camera. This reduces the number of signals to process and get only the necessary information 
  • We set a detection distance to 20 cm to reduce the influence of the background 
  • The sampling frequency of the sensor is set to its maximum (15 Hz) to capture gesture with a normal velocity 
  • We created a dataset with 1200 records per class, avoiding empty measurement (no motion). 
  • The data logging is very easy to manage with the evaluation board connected to the PC running NEAI Studio. 
  • Finally, we created an ‘N-Class classification’ model (4 classes) in NanoEdge AI Studio and tested it live on a NUCLEO_F401RE. (with a X-NUCLEO-53L5A1 add-on board) 


Time of Flight: VL53L5CX 


4 classes of data: Up, down, left and right movements
Length data: 256, 4 successive matrixes of 8×8
Data rate: 15Hz


4 classes classification:
98.12% accuracy, 1.3 KB RAM, 59.1 KB Flash

Green points represent well classified signals. Red points represent misclassified signals. The classes are on the abscissa and the confidence of the prediction is on the ordinate