WPI Computer Science Department

Computer Science Department

The AlcoGait Project Research Page

About AlcoGait & AlcoWear

Excessive alcohol use is the third leading lifestyle related cause of death in the United States. Smartphone sensing offers an opportunity to passively track alcohol usage and record associated drinking contexts. Drinkers can reflect on their drinking logs, detect patterns of abuse and self-correct or seek treatment.

AlcoGait: is a smartphone sensing app that passively detects a smartphone user's level of intoxication (how drunk) from their walk pattern (gait). AlcoGait extracts accelerometer features (time, frequency, statistical, wavelet and information theoretic domain) and gyroscope postural sway features (how much swaying) and classifies them using a Machine Learning approach.

AlcoWear: While gait sensor readings taken from a device attached to the userís trunk (smartphone) are the most accurate, users often do not carry their phones (e.g. leave them on a table) while walking around during their day. Smartwatches are worn continuously but are less accurate due to noisier sensor readings (e.g. confounding hand gestures). AlcoWear extracts and classifies accelerometer and gyroscope features extracted from a smartwatch, and classifies them using a Machine Learning approach.

Alcogait/AlcoWear Overview Alcogait/AlcoWear Architecture


Current Students

Past Students (Alumni)

Medical Collaborators


Summary: AlcoGait has received over 120 media articles including Boston Globe, 2 NPR radio articles, BBC Radio, Worcester Telegram and Gazette, Boston TV channels 2,3,4 and 5 and 7 TV appearances:




Masters Theses

Major Qualifying Projects (MQPs)


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