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

Title
Unobtrusive and Continuous Monitoring of Alcohol-impaired Gait Using Smart Shoes
Authors
Park, EunjeongLee, Sunghoon I.Nam, Hyo SukGarst, Jordan H.Huang, AlexCampion, AndrewArnell, MonicaGhalehsariand, NimaPark, SangsooChang, Hyuk-jaeLu, Daniel C.Sarrafzadeh, Majid
Ewha Authors
박상수
SCOPUS Author ID
박상수scopus
Issue Date
2017
Journal Title
METHODS OF INFORMATION IN MEDICINE
ISSN
0026-1270JCR Link
Citation
vol. 56, no. 1, pp. 74 - 82
Keywords
Wearable devicespersonal monitoringgait analysisalcohol monitoringwireless healthcare
Publisher
SCHATTAUER GMBH-VERLAG MEDIZIN NATURWISSENSCHAFTEN
Indexed
SCI; SCIE; SCOPUS WOS scopus
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.
DOI
10.3414/ME15-02-0008
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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