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Unobtrusive and Continuous Monitoring of Alcohol-impaired Gait Using Smart Shoes
- Unobtrusive and Continuous Monitoring of Alcohol-impaired Gait Using Smart Shoes
- Park, Eunjeong; Lee, Sunghoon I.; Nam, Hyo Suk; Garst, Jordan H.; Huang, Alex; Campion, Andrew; Arnell, Monica; Ghalehsariand, Nima; Park, Sangsoo; Chang, Hyuk-jae; Lu, Daniel C.; Sarrafzadeh, Majid
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- METHODS OF INFORMATION IN MEDICINE
- vol. 56, no. 1, pp. 74 - 82
- Wearable devices; personal monitoring; gait analysis; alcohol monitoring; wireless healthcare
- SCHATTAUER GMBH-VERLAG MEDIZIN NATURWISSENSCHAFTEN
- SCI; SCIE; SCOPUS
- 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.
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