Physical Activity Classification in Youth Using Raw Accelerometer Data from the Hip |
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| Authors: | Matthew N Ahmadi Karin A Pfeiffer Stewart G Trost |
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| Institution: | 1. Institute of Health and Biomedical Innovation at QLD Centre for Children’s Health Research, School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Australiamatthew.ahmadi@qut.edu.au https://orcid.org/0000-0002-3115-338X;3. Department of Kinesiology, Michigan State University, East Lansing, Michigan, USA;4. Institute of Health and Biomedical Innovation at QLD Centre for Children’s Health Research, School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Australiahttps://orcid.org/0000-0001-9587-3944 |
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| Abstract: | ABSTRACTThis study developed and evaluated machine learning algorithms to predict children’s physical activity category from raw accelerometer data collected at the hip. Fifty participants (mean age = 13.9 ± 3.0 y) completed 12 activity trials that were categorized into 5 categories: sedentary (SED), light household activities and games (LHHAG), moderate-vigorous games and sports (MVGS), walking (WALK), and running (RUN). Random Forest (RF) and Logistic Regression (LR) classifiers were trained with features extracted from the vector magnitude using 10?s non-overlapping windows. Classification accuracy was evaluated using leave-one-subject-out cross validation. Overall accuracy for the RF and LR classifiers was 95.7% and 94.3%, respectively. Classification accuracy was excellent for SED (96.3% – 98.1%), LHHAG (92.3% – 95.2%), WALK (94.5% – 97.1%), RUN (99.5% – 99.6%); and MVGS (87.5% – 92.7%). The results indicate that classifiers trained on features in the raw acceleration from the hip can be used for activity recognition in young people.Abbreviations: VM: Vector Magnitude; RF: Random Forest; LR: Logistic Regression; LOSO: Leave-One-Subject-Out |
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| Keywords: | Physical activity objective measurement accelerometry children adolescents |
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https://orcid.org/0000-0002-3115-338X;3. Department of Kinesiology, Michigan State University, East Lansing, Michigan, USA;4. Institute of Health and Biomedical Innovation at QLD Centre for Children’s Health Research, School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Australia