Unobtrusive and Continuous Monitoring of Alcohol-impaired Gait Using Smart Shoes

Eunjeong Park; Sunghoon I. Lee; Hyo Suk Nam; Jordan H. Garst; Alex Huang; Andrew Campion; Monica Arnell; Nima Ghalehsariand; Sangsoo Park; Hyuk-jae Chang; Daniel C. Lu; Majid Sarrafzadeh

doi:10.3414/ME15-02-0008

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2017; 56(01): 74-82
DOI: 10.3414/ME15-02-0008

Wearable Therapy

Schattauer GmbH

Unobtrusive and Continuous Monitoring of Alcohol-impaired Gait Using Smart Shoes

Eunjeong Park

¹Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, Korea

,

Sunghoon I. Lee

²Department of Physical Medicine & Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA

,

Hyo Suk Nam

³Department of Neurology, Yonsei University College of Medicine, Seoul, Korea

,

Jordan H. Garst

⁴Department of Neurosurgery, University of California in Los Angeles (UCLA), Los Angeles, California, USA

,

Alex Huang

⁴Department of Neurosurgery, University of California in Los Angeles (UCLA), Los Angeles, California, USA

,

Andrew Campion

⁴Department of Neurosurgery, University of California in Los Angeles (UCLA), Los Angeles, California, USA

,

Monica Arnell

⁴Department of Neurosurgery, University of California in Los Angeles (UCLA), Los Angeles, California, USA

,

Nima Ghalehsariand

⁴Department of Neurosurgery, University of California in Los Angeles (UCLA), Los Angeles, California, USA

,

Sangsoo Park

⁵Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea

,

Hyuk-jae Chang

⁶Department of Cardiology, Yonsei University College of Medicine, Seoul, Korea

,

Daniel C. Lu

⁴Department of Neurosurgery, University of California in Los Angeles (UCLA), Los Angeles, California, USA

,

Majid Sarrafzadeh

⁷Computer Science Department, University of California in Los Angeles (UCLA), Los Angeles, California, USA

› Author Affiliations

Abstract

Summary

Background: Alcohol ingestion influences sensory-motor function and the overall well-being of individuals. Detecting alcoholinduced impairments in gait in daily life necessitates a continuous and unobtrusive gait monitoring system.

Objectives: This paper introduces the development and use of a non-intrusive monitoring system to detect changes in gait induced by alcohol intoxication.

Methods: The proposed system employed a pair of sensorized smart shoes that are equipped with pressure sensors on the insole. Gait features were extracted and adjusted based on individual’s gait profile. The adjusted gait features were used to train a machine learning classifier to discriminate alcohol-impaired gait from normal walking. In experiment of pilot study, twenty participants completed walking trials on a 12 meter walkway to measure their sober walking and alcohol-impaired walking using smart shoes.

Results: The proposed system can detect alcohol-impaired gait with an accuracy of 86.2% when pressure value analysis and person-dependent model for the classifier are applied, while statistical analysis revealed that no single feature was discriminative for the detection of gait impairment.

Conclusions: Alcohol-induced gait disturbances can be detected with smart shoe technology for an automated monitoring in ubiquitous environment. We demonstrated that personal monitoring and machine learning-based prediction could be customized to detect individual variation rather than applying uniform boundary parameters of gait.

Keywords

Wearable devices - personal monitoring - gait analysis - alcohol monitoring - wireless healthcare

Full Text

References

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