Journal article
Support vector machines classifiers of physical activities in preschoolers
Physiological reports, v 1(1), pp e00006-n/a
Jun 2013
PMID: 24303099
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool‐aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3–5 years old were asked to participate in a supervised protocol of physical activities while wearing a triaxial accelerometer. Accelerometer counts, steps, and position were obtained from the device. We applied K‐means clustering to determine the number of natural groupings presented by the data. We used MLR and SVM to classify the six activity types. Using direct observation as the criterion method, the 10‐fold cross‐validation (CV) error rate was used to compare MLR and SVM classifiers, with and without sleep. Altogether, 58 classification models based on combinations of the accelerometer output variables were developed. In general, the SVM classifiers have a smaller 10‐fold CV error rate than their MLR counterparts. Including sleep, a SVM classifier provided the best performance with a 10‐fold CV error rate of 24.70%. Without sleep, a SVM classifier‐based triaxial accelerometer counts, vector magnitude, steps, position, and 1‐ and 2‐min lag and lead values achieved a 10‐fold CV error rate of 20.16% and an overall classification error rate of 15.56%. SVM supersedes the classical classifier MLR in categorizing physical activities in preschool‐aged children. Using accelerometer data, SVM can be used to correctly classify physical activities typical of preschool‐aged children with an acceptable classification error rate.
e00006
The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool‐aged children physical activity data acquired from an accelerometer. We found that SVM supersedes the classical classifier MLR in categorizing physical activities in preschool‐aged children. Using accelerometer data, SVM can be used to correctly classify physical activities typical of preschool‐aged children with an acceptable classification error rate.
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Details
- Title
- Support vector machines classifiers of physical activities in preschoolers
- Creators
- Wei Zhao - Drexel UniversityAnne L Adolph - Baylor College of MedicineMaurice R Puyau - Baylor College of MedicineFiroz A Vohra - Baylor College of MedicineNancy F Butte - Baylor College of MedicineIssa F Zakeri - Drexel University
- Publication Details
- Physiological reports, v 1(1), pp e00006-n/a
- Publisher
- Wiley
- Number of pages
- 12
- Grant note
- National Institutes of Health (R01 DK085163) USDA/ARS (58‐6250‐0‐008)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000214609900010
- Scopus ID
- 2-s2.0-85009062661
- Other Identifier
- 991014877663504721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Physiology