[1] C. Caspersen, K. Powell, and G. Christenson, “Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research”, Public health reports, Vol. 100, No. 2, pp. 126-131, 1985.
[2] D. Hendelman, K. Miller, C. Bagget, E. Debold, and P. Freedson, “Validity of accelerometry for the assessment of moderate intensity physical activity in the field”, Medicine and Science in Sports and Exercise, Vol. 32, No. 9, pp. 442-449, 2000.
[3] L. Bao, and S. Intille, “Activity recognition from user-annotated acceleration data”, Pervasive Computing, Vol. 3001, pp. 1-17, 2004.
[4] M. Ermes, J. Parkka, J. Mantyjarvi, and I. Korhonen, “Detection of daily activities and sports with wearable sensors in controlled and uncontrolled condi-tions”, IEEE Transactions on Information Technology in Biomedicine, Vol. 12, No. 1, pp. 20-26, 2008.
[5] S. Liu, R. Gao, and P. Freedson, “Design of a wearable multi-sensor system for physical activity assessment”, Proc. IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics, pp. 254-259, 2010.
[6] J. Lester, T. Choudhury, and G. Borriello, “A practical approach to recognizing physical activities”, Pervasive Computing, pp. 1-16, 2006.
[7] L. Breiman, “Bagging predictors”, Machine learning, Vol.24, No. 2, pp. 123-140, 1996.
[8] N. Ravi, N. Dandekar, P. Mysore, and M. Littman, “Activity recognition from accelerometer data”, Proceedings of the Seventeenth Conference on Innova-tive Applications of Artificial Intelligence(IAAI 2005), pp. 1541-1546, 2005.
[9] M. Gashler, C. Giraud-Carrier, and T. Martinez, “Decision tree ensemble: small heterogeneous is better than large homogeneous”, IEEE Seventh International Conference on Machine Learning and Applications, San Diego, CA, USA, pp. 900-905, 2008.
[10] D. Opitz, and R. Maclin, “Popular ensemble methods: An empirical study”, Journal of Artificial Intelligence Research, Vol. 11, No. 1, pp. 169-198, 1999.