Using deep learning for alcohol consumption recognition

Abstract

As Convolutional Neural Networks continue to produce state of the art results, more types of data are being used to see the results that would be produced. Using the heart rate data that was collected using sensors from various subjects who consumed alcohol, we converted it from the 1D waveform into a set of spectrograms. The spectrograms were fed into two pretrained CNNs, CaffeNet and AlexNet, to determine whether or not the given spectrogram was an instance of alcohol consumption. Using 80 training images (40 positive, 40 negative) and 20 test images (10 positive, 10 negative), we achieved a test accuracy, after adjusting learning rate, number of iterations, and gradient descent algorithm, as well as the time window and coloration of the spectrograms, of 72% (n=20, 5 trials), which are promising results for non-audio waveforms.