Fine-Grained Intoxicated Gait Classification Using a Bilinear CNN-Reference-Cited by-同舟云学术

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Fine-Grained Intoxicated Gait Classification Using a Bilinear CNN

Published:2023-12-01 Issue:23 Volume:23 Page:29733-29748
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Li Ruojun¹,Agu Emmanuel²^ORCID,Sarwar Atifa²^ORCID,Grimone Kristin³,Herman Debra⁴,Abrantes Ana M.⁴,Stein Michael D.⁵

Affiliation:

1. Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA

2. Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, USA

3. Department of Behavioral Medicine and Addictions Research, Butler Hospital, Providence, RI, USA

4. Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA

5. Department of Health Law, Policy and Management, Boston University, Boston, MA, USA

Funder

National Institutes of Health (NIH)/National Institute on Alcohol Abuse and Alcoholism

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10336249/10097599.pdf?arnumber=10097599

Reference64 articles.

1. The Study of Drunken Abnormal Human Gait Recognition using Accelerometer and Gyroscope Sensors in Mobile Application

2. Validation of Freezing-of-Gait Monitoring Using Smartphone

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

4. Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network

5. Forecasting with time series imaging

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