AlcoGait Research
Overview
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
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 user’s 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.
News
Too High, Drunk, or Sleepy to Drive? One Day Your Phone Could Know
Eventually, phone-based sobriety tests could tell you if you're too drunk, stoned, or sleepy to drive....
WPI develops an app to make drunk drivers toe the line - The Boston Globe
Engineers at Worcester Polytechnic Institute are developing an Android app that will monitor the way drinkers walk....
WPI students develop app as sobriety test
Brainiacs at Worcester Polytechnic Institute have developed a first-of-its-kind smartphone app that can tell its user if he or she has had too much alcohol to drink without the use of blo…...