Remote sensing of alcohol consumption using machine learning speckle pattern analysis

Abstract

Significance: Alcohol consumption monitoring is essential for forensic and healthcare applications. While breath and blood alcohol concentration sensors are currently the most common methods, there is a growing need for faster, non-invasive, and more efficient assessment techniques. The rationale for our binary classification relates to law enforcement applications in countries with strict limits on alcohol consumption such as China, which seeks to prevent driving with even the smallest amount of alcohol in the bloodstream.

Aim: We propose a remote optical technique for assessing alcohol consumption using speckle pattern analysis, enhanced by machine learning for binary classification. This method offers remote and fast alcohol consumption evaluation without requiring before and after comparisons.

Approach: Our experimental setup includes a laser directed toward the subject's radial artery, a camera capturing defocused speckle pattern images of the illuminated area, and a computer. Participants consumed alcohol and were tested periodically. We developed a machine learning classification model that performs automatic feature selection based on temporal analysis of the speckle patterns. The model was evaluated using various labeling schemes: classification with five labels, consolidation to three labels by merging similar labels, and three different binary classifications cases ("Alcohol" or "No alcohol").

Results: Our classification models showed improving accuracy as we reduced the number of labels. The initial five-label model achieved 61% accuracy. When consolidated into three labels, the models achieved accuracies of 74% and 85% for the two cases. The binary classification models performed best, with model A achieving 91% accuracy and 97% specificity, model B achieving 83% accuracy, and model C achieving 88% accuracy with 99% sensitivity.

Conclusions: Our binary classification model C can successfully distinguish between pre- and post-alcohol consumption with high sensitivity and accuracy. This performance is particularly valuable for clinical and forensic applications, where minimizing false negatives is crucial.

Keywords: alcohol; machine learning; remote sensing; speckle.

Fig. 1

(a) Illustration of the PCG signal and (b) the optical setup for alcohol consumption sensing.

Fig. 2

Workflow of the speckle pattern image processing for alcohol consumption estimation.

Fig. 3

Workflow of the machine learning model for alcohol consumption estimation.

Fig. 4

Model of five labels. (a) Confusion matrix and (b) ROCs.

Fig. 5

Model of three labels. Confusion matrix (a) for the first case and (b) for the second case.

Fig. 6

Models of two labels. Confusion matrix; (a) model A, (b) model B, (c) model C, and ROC curves; (d) model A, (e) model B, (f) model C.