Next Article in Journal
Low Cost Plastic Optical Fiber Pressure Sensor Embedded in Mattress for Vital Signal Monitoring
Previous Article in Journal
Performance Analysis of Cluster Formation in Wireless Sensor Networks
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(12), 2897; doi:10.3390/s17122897

An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level

1
Department of Informatics and Networked Systems, University of Pittsburgh School of Computing and Information, Pittsburgh, PA 15260, USA
2
Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
3
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
*
Author to whom correspondence should be addressed.
Received: 22 October 2017 / Revised: 2 December 2017 / Accepted: 8 December 2017 / Published: 13 December 2017
(This article belongs to the Section Intelligent Sensors)
View Full-Text   |   Download PDF [4383 KB, uploaded 19 December 2017]   |  

Abstract

Impairments in gait occur after alcohol consumption, and, if detected in real-time, could guide the delivery of “just-in-time” injury prevention interventions. We aimed to identify the salient features of gait that could be used for estimating blood alcohol content (BAC) level in a typical drinking environment. We recruited 10 young adults with a history of heavy drinking to test our research app. During four consecutive Fridays and Saturdays, every hour from 8 p.m. to 12 a.m., they were prompted to use the app to report alcohol consumption and complete a 5-step straight-line walking task, during which 3-axis acceleration and angular velocity data was sampled at a frequency of 100 Hz. BAC for each subject was calculated. From sensor signals, 24 features were calculated using a sliding window technique, including energy, mean, and standard deviation. Using an artificial neural network (ANN), we performed regression analysis to define a model determining association between gait features and BACs. Part (70%) of the data was then used as a training dataset, and the results tested and validated using the rest of the samples. We evaluated different training algorithms for the neural network and the result showed that a Bayesian regularization neural network (BRNN) was the most efficient and accurate. Analyses support the use of the tandem gait task paired with our approach to reliably estimate BAC based on gait features. Results from this work could be useful in designing effective prevention interventions to reduce risky behaviors during periods of alcohol consumption. View Full-Text
Keywords: neural network; Bayesian regularization neural network (BRNN); blood alcohol content (BAC); feature extraction; Gait analysis neural network; Bayesian regularization neural network (BRNN); blood alcohol content (BAC); feature extraction; Gait analysis
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Gharani, P.; Suffoletto, B.; Chung, T.; Karimi, H.A. An Artificial Neural Network for Movement Pattern Analysis to Estimate Blood Alcohol Content Level. Sensors 2017, 17, 2897.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top