Posture and Activity Recognition and Energy Expenditure Estimation in a Wearable Platform

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dc.contributor.authorSazonov, Edward
dc.contributor.authorHegde, Nagaraj
dc.contributor.authorBrowning, Raymond C.
dc.contributor.authorMelanson, Edward L.
dc.contributor.authorSazonova, Nadezhda A.
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.contributor.otherColorado State University
dc.contributor.otherUniversity of Colorado Denver
dc.date.accessioned2023-09-28T19:31:07Z
dc.date.available2023-09-28T19:31:07Z
dc.date.issued2015
dc.description.abstractThe use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here, we describe the use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time recognition of various postures/physical activities and the resulting EE. To deal with processing power and memory limitations of the phone, we compare the use of support vector machines (SVM), multinomial logistic discrimination (MLD), and multilayer perceptrons (MLP) for posture and activity classification followed by activity-branched EE estimation. The algorithms were validated using data from 15 subjects who performed up to 15 different activities of daily living during a 4-h stay in a room calorimeter. MLD and MLP demonstrated activity classification accuracy virtually identical to SVM (similar to 95%) while reducing the running time and the memory requirements by a factor of >10(3). Comparison of per-minute EE estimation using activity-branched models resulted in accurate EE prediction (RMSE = 0.78 kcal/min for SVM andMLD activity classification, 0.77 kcal/min for MLP versus RMSE of 0.75 kcal/min for manual annotation). These results suggest that low-power computational algorithms can be successfully used for real-time physical activity monitoring and EE estimation on a wearable platform.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationSazonov, E., Hegde, N., Browning, R. C., Melanson, E. L., & Sazonova, N. A. (2015). Posture and Activity Recognition and Energy Expenditure Estimation in a Wearable Platform. In IEEE Journal of Biomedical and Health Informatics (Vol. 19, Issue 4, pp. 1339–1346). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/jbhi.2015.2432454
dc.identifier.doi10.1109/JBHI.2015.2432454
dc.identifier.orcidhttps://orcid.org/0000-0003-3382-7939
dc.identifier.orcidhttps://orcid.org/0000-0001-7792-4234
dc.identifier.urihttps://ir.ua.edu/handle/123456789/11363
dc.languageEnglish
dc.language.isoen_US
dc.publisherIEEE
dc.subjectEnergy expenditure
dc.subjectphysical activity
dc.subjectshoe sensors
dc.subjectwearable sensors
dc.subjectPHYSICAL-ACTIVITY
dc.subjectSENSORS
dc.subjectGAIT
dc.subjectComputer Science, Information Systems
dc.subjectComputer Science, Interdisciplinary Applications
dc.subjectMathematical & Computational Biology
dc.subjectMedical Informatics
dc.titlePosture and Activity Recognition and Energy Expenditure Estimation in a Wearable Platformen_US
dc.typeArticle
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