updown, the sample length to
10000, the sensor to
Built-in accelerometerand the frequency to
62.5Hz. This indicates that you want to record data for 10 seconds, and label the recorded data as
updown. You can later edit these labels if needed.
2000(you can click on the
2000 ms.text to enter an exact value), the window increase to
80, and add the 'Spectral Analysis' and 'Classification (Keras)' blocks. Then click Save impulse.
wave(one the classes). When defining the neural network all these connections are initialized randomly, and thus the neural network will make random predictions. During training, we then take all the raw data, ask the network to make a prediction, and then make tiny alterations to the weights depending on the outcome (this is why labeling raw data is important).
1.. This will limit training to a single iteration. And then click Start training.
2and you'll see performance go up. Finally, change 'Number of training cycles' to
100and let training finish. You've just trained your first neural network!
5000(5 seconds), click Start sampling and start doing movements. Afterward, you'll get a full report on what the network thought that you did.
⋮, then selecting Move to training set. Make sure to update the label under 'Data acquisition' before training.
200and see if performance increases (the classified file is stored, and you can load it through 'Classify existing validation sample').