Faculty Advisor

Agu, Emmanuel O.


Alcohol abuse has been a pervasive problem worldwide, causing 88,000 annual deaths. Recently, several projects have attempted to estimate a user’s level of intoxication by measuring gait using mobile sensors. The goal of this project was to compare a deep learning approach to previous methods to predict the blood alcohol concentration of a user by training a convolutional neural network and creating a mobile app which could accurately determine intoxication level. We gathered data from 38 participants over the course of 12 weeks, collecting accelerometer and gyroscope data simultaneously from both a smartwatch and smartphone. Our neural network’s accuracy is roughly 64% on the test set and 69% on the training set into 5 BAC ranges for an input containing two seconds of data.


Worcester Polytechnic Institute

Date Accepted

March 2018


Computer Science

Project Type

Major Qualifying Project



Advisor Department

Computer Science