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Related Experiment Video

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Web-based Clinician Guide to Record Compatible Video of Standardized Drinking Task Kinematics for Computer Vision Analysis
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An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level.

Pedram Gharani1, Brian Suffoletto2, Tammy Chung3

  • 1Department of Informatics and Networked Systems, University of Pittsburgh School of Computing and Information, Pittsburgh, PA 15260, USA. peg25@pitt.edu.

Sensors (Basel, Switzerland)
|December 14, 2017
PubMed
Summary

Gait analysis using sensors can estimate blood alcohol content (BAC) in real-time. This technology could enable timely interventions to prevent alcohol-related injuries.

Keywords:
Bayesian regularization neural network (BRNN)Gait analysisblood alcohol content (BAC)feature extractionneural network

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Public Health

Background:

  • Alcohol consumption impairs gait, increasing injury risk.
  • Real-time detection of gait impairment could facilitate "just-in-time" injury prevention.
  • Identifying gait features for blood alcohol content (BAC) estimation is crucial.

Purpose of the Study:

  • To identify salient gait features for estimating BAC in a naturalistic drinking setting.
  • To develop and validate a model for real-time BAC estimation using gait data.
  • To explore the potential for gait analysis in alcohol-related injury prevention.

Main Methods:

  • Recruited 10 young adults with heavy drinking histories.
  • Collected 3-axis acceleration and angular velocity gait data via a research app.
  • Utilized an artificial neural network (ANN), specifically Bayesian Regularization Neural Network (BRNN), for BAC estimation from 24 gait features.

Main Results:

  • The BRNN model accurately estimated BAC based on gait features.
  • Tandem gait task data, combined with the developed approach, reliably predicted BAC.
  • The study demonstrated the feasibility of using sensor-based gait analysis for BAC estimation.

Conclusions:

  • Gait analysis offers a promising method for non-invasive, real-time BAC estimation.
  • This technology can support the development of targeted interventions to mitigate risks associated with alcohol consumption.
  • Further research can refine gait-based BAC monitoring for public health applications.