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

Authors
 E. Park  ;  S. I. Lee  ;  H. S. Nam  ;  J. H. Garst  ;  A. Huang  ;  A. Campion  ;  M. Arnell  ;  N. Ghalehsariand  ;  S. Park  ;  H.-j. Chang  ;  D. C. Lu  ;  M. Sarrafzadeh 
Citation
 METHODS OF INFORMATION IN MEDICINE, Vol.56(1) : 74-82, 2017 
Journal Title
 METHODS OF INFORMATION IN MEDICINE 
ISSN
 0026-1270 
Issue Date
2017
MeSH
Adult ; Alcohols/adverse effects* ; Algorithms ; Female ; Gait/physiology* ; Humans ; Male ; Monitoring, Ambulatory* ; Pressure ; Shoes*
Keywords
Wearable devices ; alcohol monitoring ; gait analysis ; personal monitoring ; wireless healthcare
Abstract
BACKGROUND: Alcohol ingestion influences sensory-motor function and the overall well-being of individuals. Detecting alcohol-induced 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.
Full Text
https://methods.schattauer.de/en/contents/archivestandard/issue/2442/manuscript/26751.html
DOI
10.3414/ME15-02-0008
Appears in Collections:
2. College of Dentistry (치과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Nam, Hyo Suk(남효석) ORCID logo https://orcid.org/0000-0002-4415-3995
Park, Eunjeong(박은정)
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/153341
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