Faculty Advisor

Agu, Emmanuel O.

Faculty Advisor

Petruccelli, Joseph D.

Abstract

An Android smartphone app was developed to determine alcohol intoxication levels based on a user’s gait. It uses the phone's accelerometer to detect differences in gait associated with varying alcohol ingestion levels. Signal analysis found features in the time and frequency domain indicative of intoxication levels. Machine learning methods were employed with these features, trained on data from subjects, and compared by performance on a validation set. The Random Forest method was the best classifier with a success rate of 56% on the training set and 70% on the validation set. The app was distributed for user testing and included the model to be trained with the user’s new data. Accuracy improved overall with the users to 57%. As the app is used more, the accuracy is expected to increase.

Publisher

Worcester Polytechnic Institute

Date Accepted

April 2015

Major

Computer Science

Major

Mathematical Sciences

Project Type

Major Qualifying Project

Accessibility

Unrestricted

Advisor Department

Computer Science

Advisor Department

Mathematical Sciences

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