PMID- 38186565
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240109
IS  - 1530-437X (Print)
IS  - 1530-437X (Linking)
VI  - 23
IP  - 23
DP  - 2023 Dec
TI  - Fine-Grained Intoxicated Gait Classification Using a Bilinear CNN.
PG  - 29733-29748
LID - 10.1109/jsen.2023.3248868 [doi]
AB  - Consuming excessive amounts of alcohol causes impaired mobility and judgment and 
      driving accidents, resulting in more than 800 injuries and fatalities each day. 
      Passive methods to detect intoxicated drivers beyond the safe driving limit can 
      facilitate Just-In-Time alerts and reduce Driving Under the Influence (DUI) 
      incidents. Popularly-owned smartphones are not only equipped with motion sensors 
      (accelerometer and gyroscope) that can be employed for passively collecting gait 
      (walk) data but also have the processing power to run computationally expensive 
      machine learning models. In this paper, we advance the state-of-the-art by 
      proposing a novel method that utilizes a Bi-linear Convolution Neural Network 
      (BiCNN) for analyzing smartphone accelerometer and gyroscope data to determine 
      whether a smartphone user is over the legal driving limit (0.08) from their gait. 
      After segmenting the gait data into steps, we converted the smartphone motion 
      sensor data to a Gramian Angular Field (GAF) image and then leveraged the BiCNN 
      architecture for intoxication classification. Distinguishing GAF-encoded images 
      of the gait of intoxicated vs. sober users is challenging as the differences 
      between the classes (intoxicated vs. sober) are subtle, also known as a 
      fine-grained image classification problem. The BiCNN neural network has 
      previously produced state-of-the-art results on fine-grained image classification 
      of natural images. To the best of our knowledge, our work is the first to 
      innovatively utilize the BiCNN to classify GAF encoded images of smartphone gait 
      data in order to detect intoxication. Prior work had explored using the BiCNN to 
      classify natural images or explored other gait-related tasks but not intoxication 
      Our complete intoxication classification pipeline consists of several important 
      pre-processing steps carefully adapted to the BAC classification task, including 
      step detection and segmentation, data normalization to account for inter-subject 
      variability, data fusion, GAF image generation from time-series data, and a BiCNN 
      classification model. In rigorous evaluation, our BiCNN model achieves an 
      accuracy of 83.5%, outperforming the previous state-of-the-art and demonstrating 
      the feasibility of our approach.
FAU - Li, Ruojun
AU  - Li R
AD  - Department of Optical Information, Huazhong University of Science and Technology, 
      Wuhan, China.
AD  - Department of Electrical and Computer Engineering, Worcester Polytechnic 
      Institute(WPI), Worcester, MA, USA.
FAU - Agu, Emmanuel
AU  - Agu E
AD  - Computer Science Department, Worcester Polytechnic Institute, Worcester, MA, USA.
FAU - Sarwar, Atifa
AU  - Sarwar A
AD  - computer science with Magna Cum Laude.
FAU - Grimone, Kristin
AU  - Grimone K
AD  - Ohio State University.
FAU - Herman, Debra
AU  - Herman D
AD  - Department of Psychiatry and Human Behavior and a Research Psychologist in the 
      Behavioral Medicine and Addictions Research group at Butler Hospital.
FAU - Abrantes, Ana M
AU  - Abrantes AM
AD  - Behavioral Medicine and Addictions Research at Butler Hospital and a Professor in 
      the Department of Psychiatry and Human Behavior at the Alpert Medical School of 
      Brown University.
FAU - Stein, Michael D
AU  - Stein MD
AD  - Chair of Health Law, Policy & Management at Boston University.
LA  - eng
GR  - R21 AA025193/AA/NIAAA NIH HHS/United States
PT  - Journal Article
DEP - 20230407
PL  - United States
TA  - IEEE Sens J
JT  - IEEE sensors journal
JID - 101212357
PMC - PMC10769125
MID - NIHMS1948738
OTO - NOTNLM
OT  - Blood Alchol Content (BAC)
OT  - Convolutional Neural Networks (CNNs)
OT  - Gait Analysis
OT  - Neural Networks
EDAT- 2024/01/08 06:42
MHDA- 2024/01/08 06:43
PMCR- 2024/12/01
CRDT- 2024/01/08 04:26
PHST- 2024/12/01 00:00 [pmc-release]
PHST- 2024/01/08 06:43 [medline]
PHST- 2024/01/08 06:42 [pubmed]
PHST- 2024/01/08 04:26 [entrez]
AID - 10.1109/jsen.2023.3248868 [doi]
PST - ppublish
SO  - IEEE Sens J. 2023 Dec;23(23):29733-29748. doi: 10.1109/jsen.2023.3248868. Epub 
      2023 Apr 7.