Physical Activity Classification in Youth Using Raw Accelerometer Data from the Hip

References

• Ahmadi, M., Neil, M. O., Fragala-pinkham, M., Lennon, N., & Trost, S. (2018). Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy. Journal of Neuroengineering and Rehabilitation, 15(105), 1–9. doi:10.1186/s12984-018-0456-x
   PubMedGoogle Scholar
• Aubert, S., Barnes, J. D., Abdeta, C., Nader, P. A., Adeniyi, A. F., Aguilar-Farias, N., … Tremblay, M. S. (2018). Global matrix 3.0 physical activity report card grades for children and youth: Results and analysis from 49 Countries. Journal of Physical Activity and Health, 15(S2), S251–S273. doi:10.1123/jpah.2018-0472
   PubMed Web of Science ®Google Scholar
• Bassett, D. R., Rowlands, A., & Trost, S. G. (2012). Calibration and validation of wearable monitors. Medicine and Science in Sports and Exercise, 44(SUPPL), 1. doi:10.1249/MSS.0b013e3182399cf7
   PubMedGoogle Scholar
• Bastian, T., Maire, A., Dugas, J., Ataya, A., Villars, C., Gris, F., … Simon, C. (2015). Automatic identification of physical activity types and sedentary behaviors from triaxial accelerometer: Laboratory-based calibrations are not enough. Journal of Applied Physiology (Bethesda, Md. : 1985), 118(6), 716–722. doi:10.1152/japplphysiol.01189.2013
   PubMed Web of Science ®Google Scholar
• Chen, K. Y., Janz, K. F., Zhu, W., & Brychta, R. J. (2012). Re-defining the roles of sensors in objective physical activity monitoring. Medicine and Science in Sports and Exercise, 44(S1), S1–S23. doi:10.1249/MSS.0b013e3182399bc8.
   PubMedGoogle Scholar
• Cliff, D. P., McNeill, J., Vella, S. A., Howard, S. J., Santos, R., Batterham, M., … de Rosnay, M. (2017). Adherence to 24-hour movement guidelines for the early years and associations with social-cognitive development among Australian preschool children. BMC Public Health, 17(S5), 857. doi:10.1186/s12889-017-4858-7
   PubMed Web of Science ®Google Scholar
• Cooper, A. R., Goodman, A., Page, A. S., Sherar, L. B., Esliger, D. W., van Sluijs, E. M., … Ekelund, U. (2015). Objectively measured physical activity and sedentary time in youth: The international children’s accelerometry database (ICAD). International Journal of Behavioral Nutrition and Physical Activity, 12(1), 113. doi:10.1186/s12966-015-0274-5
   PubMed Web of Science ®Google Scholar
• Corder, K., Ekelund, U., Steele, R. M., Wareham, N. J., & Brage, S. (2008). Assessment of physical activity in youth. Journal of Applied Physiology, 105, 977–987. doi:10.1152/japplphysiol.00094.2008
   PubMed Web of Science ®Google Scholar
• Crouter, S. E., Horton, M., & Bassett, D. R. (2012). Use of a two-regression model for estimating energy expenditure in children. Medicine and Science in Sports and Exercise, 44(6), 1177–1185. doi:10.1249/MSS.0b013e3182447825
   PubMed Web of Science ®Google Scholar
• DeVries, S., Engels, M., & Garre, F. G. (2011). Identification of children’s activity type with accelerometer-based neural networks. Medicine and Science in Sports and Exercise, 43(10), 1994–1999. doi:10.1249/MSS.0b013e318219d939
   PubMed Web of Science ®Google Scholar
• Forman, G., & Scholz, M. (2010). Apples-to-apples in cross-validation studies: Pitfalls in classifier performance measurement. ACM SIGKDD Explorations Newsletter, 12(1), 49–57. doi:10.1145/1882471.1882479
   Google Scholar
• Ha, A. S., Ng, J. Y. Y., Lonsdale, C., Lubans, D. R., & Ng, F. F. (2019). Promoting physical activity in children through family-based intervention: Protocol of the “Active 1 + FUN” randomized controlled trial 11 medical and health sciences 1117 public health and health services 17 psychology and cognitive sciences 1701 psychol. BMC Public Health, 19(1), 1–12. doi:10.1186/s12889-019-6537-3
   PubMedGoogle Scholar
• Hagenbuchner, M., Cliff, D. P., Trost, S. G., Van Tuc, N., & Peoples, G. E. (2015). Prediction of activity type in preschool children using machine learning techniques. Journal of Science & Medicine in Sport, 18(4), 426–431. doi: 10.1016/j.jsams.2014.06.003.
   PubMed Web of Science ®Google Scholar
• Hallal, P. C., Andersen, L. B., Bull, F. C., Guthold, R., & Haskell, W. (2012). Physical activity levels of the world ’ s population surveillance progress, gaps and prospects. The Lancet, 380, 247–257. doi:10.1016/S0140-6736(12)60646-1
   PubMed Web of Science ®Google Scholar
• Heil, D. P. (2006). Predicting activity energy expenditure using the actical activity monitor. Research Quarterly for Exercise and Sport, 77, 64–80. doi:10.1080/02701367.2006.10599333
   PubMed Web of Science ®Google Scholar
• HHS. Physical Activity Guidelines Advisory Committee. (2018). 2018 physical activity guidelines advisory committee scientific report. In:. US Department of Health and Human Services.
   Google Scholar
• Hislop, J., Bulley, C., Mercer, T., & Reilly, J. J. (2012). Comparison of accelerometry cut points for physical activity and sedentary behavior in preschool children: A validation study. Pediatric Exercise Science, 24(4), 563–576. doi:10.1123/pes.24.4.563
   PubMed Web of Science ®Google Scholar
• Ipsos, M. O. R. I. (2017). CLS. Millennium Cohort Study Sixth Sweep (MCS6): Physical Activity: Time Use Diary Harmonised Dataset. London, UK: Centre for Longitudinal Studies. UCL Institute for Education, 134.
   Google Scholar
• Kühnhausen, J., Dirk, J., & Schmiedek, F. (2016). Individual classification of elementary school children’s physical activity: A time-efficient, group-based approach to reference measurements. Behavior Research Methods, 685–697. doi:10.3758/s13428-016-0724-2
   Web of Science ®Google Scholar
• Liu, S., Gao, R. X., & Freedson, P. S. (2012). Computational methods for estimating energy expenditure in human physical activities. Medicine and Science in Sports and Exercise, 44(11), 2138–2146. doi:10.1249/MSS.0b013e31825e825a
   PubMed Web of Science ®Google Scholar
• Mitchell, T. M. (2010). Chapter 1 generative and discriminative classifiers : Naive bayes and logistic regression learning classifiers based on bayes rule. Machine Learning, 1(Pt 1–2), 1–17. doi:10.1093/bioinformatics/btq112
   Google Scholar
• Nam, Y., & Park, J. W. (2013). Child activity recognition based on cooperative fusion model of a triaxial accelerometer and a barometric pressure sensor. IEEE Journal of Biomedical and Health Informatics, 17(2), 420–426. doi:10.1109/Jbhi.2012.2235075
   PubMed Web of Science ®Google Scholar
• Nyquist, H. (1928). Certain topics in telegraph transmission theory. Trans AIEE, 47(2), 617–644.
   Google Scholar
• Plasqui, G. (2017). Smart approaches for assessing free-living energy expenditure following identification of types of physical activity. Obesity Reviews, 18(February), 50–55. doi:10.1111/obr.12506
   PubMedGoogle Scholar
• Puyau, M. R., Adolph, A. L., Vohra, F. A., Butte, N. F., Maurice, R., Adolph, A. L., … Validation, N. F. B. (2002). Validation and calibration of physical activity monitors in children. Obesity Research, 10, 3. doi:10.1038/oby.2002.24
   Google Scholar
• Reiss, A., Weber, M., & Stricker, D. (2011). Exploring and extending the boundaries of physical activity recognition. In IEEE International Conference on Systems, Man, and Cybernetics (pp. 46–50). doi:10.1109/ICSMC.2011.6083640
   Google Scholar
• Ruch, N., Joss, F., Jimmy, G., Melzer, K., Hänggi, J., & Mäder, U. (2013). Neural network versus activity-specific prediction equations for energy expenditure estimation in children. Journal of Applied Physiology (Bethesda, Md.: 1985), 115(9), 1229–1236. doi:10.1152/japplphysiol.01443.2012
   PubMed Web of Science ®Google Scholar
• Ruch, N., Rumo, M., & Mäder, U. (2011). Recognition of activities in children by two uniaxial accelerometers in free-living conditions. European Journal of Applied Physiology, 111(8), 1917–1927. doi:10.1007/s00421-011-1828-0
   PubMed Web of Science ®Google Scholar
• Strong, W. B., Malina, R. M., Blimkie, C. J. R., Daniels, S. R., Dishman, R. K., Gutin, B., … Trudeau, F. (2005). Evidence based physical activity for school-age youth. The Journal of Pediatrics, 146(6), 732–737. doi:10.1016/j.jpeds.2005.01.055
   PubMed Web of Science ®Google Scholar
• Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. doi:10.1111/j.2517-6161.1996.tb02080.x
   Web of Science ®Google Scholar
• Treuth, M. S., Schmitz, K., Catellier, D. J., McMurray, R. G., Murray, D. M., Almeida, M. J., … Pate, R. (2004). Defining accelerometer thresholds for activity intensities in adolescent girls. Medicine and Science in Sports and Exercise, 36(7), 1259–1266. doi:10.1249/01.MSS.0000074670.03001.98
   PubMed Web of Science ®Google Scholar
• Troiano, R. P., Mcclain, J. J., Brychta, R. J., & Chen, K. Y. (2014). Evoluation of accelerometer methods for physical activity research. British Journal of Sports Medicine, 100(2), 130–134. doi:10.1016/j.pestbp.2011.02.012.Investigations
   Google Scholar
• Trost, S. G. (2007). Measurement of physical activity in children and adolescents. American Journal of Lifestyle Medicine, 1(4), 299–314. doi:10.1177/1559827607301686
   Google Scholar
• Trost, S. G., Cliff, D. P., Ahmadi, M. N., Van, T. N., & Hagenbuchner, M. (2018). Sensor-enabled activity class recognition in preschoolers: Hip versus wrist data. Medicine and Science in Sports and Exercise, 50(3), 634–641. doi:10.1249/MSS.0000000000001460
   PubMed Web of Science ®Google Scholar
• Trost, S. G., Loprinzi, P. D., Moore, R., & Pfeiffer, K. A. (2011). Comparison of accelerometer cut points for predicting activity intensity in youth. Medicine and Science in Sports and Exercise, 43(7), 1360–1368. doi:10.1249/MSS.0b013e318206476e
   PubMed Web of Science ®Google Scholar
• Trost, S. G., Wong, W.-K., Pfeiffer, K. A., & Zheng, Y. (2012). Artificial neural networks to predict activity type and energy expenditure in youth. Medicine and Science in Sports and Exercise, 44(9), 1801–1809. doi:10.1249/MSS.0b013e318258ac11
   PubMed Web of Science ®Google Scholar
• Trost, S. G., Zheng, Y., & Wong, W.-K. (2014). Machine learning for activity recognition: Hip versus wrist data. Physiological Measurement, 35(11), 2183–2189. doi:10.1088/0967-3334/35/11/2183
   PubMed Web of Science ®Google Scholar
• Tudor-Locke, C., Barreira, T. V., Schuna, J. M., & Katzmarzyk, P. T. (2015). Unique contributions of ISCOLE to the advancement of accelerometry in large studies. International Journal of Obesity Supplements, 5(S2), S53–S58. doi:10.1038/ijosup.2015.20
   PubMedGoogle Scholar
• Vale, S., Trost, S. G., Rêgo, C., Abreu, S., & Mota, J. (2015). Physical activity, obesity status, and blood pressure in preschool children. The Journal of Pediatrics, 167(1), 98–102. doi:10.1016/j.jpeds.2015.04.031
   PubMed Web of Science ®Google Scholar
• Wake, M., Clifford, S., York, E., Mensah, F., Gold, L., Burgner, D., … Zubrick, S. (2014). Introducing growing up in Australia’s child health checkpoint: A physical health and biomarkers module for the longitudinal study of Australian children. Family Matters, 94, 15–23.
   Google Scholar
• WHO. (2018). Global action plan on physical activity 2018–2030: More active people for a healthier world. Geneva: World Health Organization. 2018. Licence: CC BY-NC-SA 3.0 IG. Who.
   Google Scholar
• Wijndaele, K., Westgate, K., Stephens, S. K., Blair, S. N., Bull, F. C., Chastin, S. F. M., … Healy, G. N. (2015). Utilization and harmonization of adult accelerometry data: Review and expert consensus. Medicine and Science in Sports and Exercise, 47(10), 2129–2139. doi:10.1249/MSS.0000000000000661
   PubMed Web of Science ®Google Scholar
• Zhang, S., Murray, P., Zillmer, R., Eston, R. G., Catt, M., & Rowlands, A. V. (2012). Activity classification using the genea: Optimum sampling frequency and number of axes. Medicine and Science in Sports and Exercise, 44(11), 2228–2234. doi:10.1249/MSS.0b013e31825e19fd
   PubMed Web of Science ®Google Scholar
• Zhao, W., Adolph, A. L., Puyau, M. R., Vohra, F. A., Butte, N. F., & Zakeri, I. F. (2013). Support vector machines classifiers of physical activities in preschoolers. Physiological Reports, 1(1), 1–12. doi:10.1002/phy2.6
   Google Scholar