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.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Arnold, Zachary Philip
• LaRose, Danielle Marie

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-043015-100131

Award

• Provost's MQP Award

Advisor

• Agu, Emmanuel O.
• Petruccelli, Joseph D.

Year

• 2015

Date created

• 2015-04-30

Resource type

• Major Qualifying Project

Major

• Interdisciplinary

Rights statement

• In Copyright

Last modified

• 2023-01-20