A Comprehensive Comparative Performance Evaluation of Signal Processing Features in Detecting Alcohol Consumption from Gait Data

Excessive alcohol is the third leading lifestyle-related cause of death in the United States. Alcohol intoxication has a significant effect on how the human body operates, and is especially harmful to the human brain and heart. To help individuals to monitor their alcohol intoxication, several methods have been proposed to detect alcohol consumption levels including direct Blood Alcohol Concentration (BAC) measurement by breathalyzers and various wearable sensor devices. More recently, Arnold et al proposed a machine-learning-based method of passively inferring intoxication levels from gait data by classifying smartphone accelerometer readings. Their work utilized 11 smartphone accelerometer features in the time and frequency domains, achieving a classification accuracy of 57%. This thesis extends the work of Arnold et al by extracting and comparing the efficacy of a more comprehensive list of 27 signal processing features in the time, frequency, wavelet, statistical and information theory domains, evaluating how much using them improves the accuracy of supervised BAC classification of accelerometer gait data. Correlation-based Feature Selection (CFS) is used to identify and rank features most correlated with alcohol-induced gait changes. 22 of the 27 features investigated showed statistically significant correlations with BAC levels. The most correlated features were then used to classify labeled samples of intoxicated gait data in order to test their detection accuracy. Statistical features had the best classification accuracy of 83.89%, followed by time domain features and frequency domain features follow with accuracies of 83.22% and 82.21%, respectively. Classification using all 22 statistically significant signal processing features yielded an accuracy of 84.9% for the Random Forest classifier