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