W.H.O. Obesity and overweight
. Fact Sheets 2013 [cited 2014 March 13]; Available from: http://www.who.int/mediacentre/factsheets/fs311/en/
Abdullah, A., et al., The number of years lived with obesity and the risk of all-cause and cause-specific mortality. International journal of epidemiology
40(4):985–996, 2011.CrossRefMathSciNetGoogle Scholar
Villareal, D. T., et al., Weight loss, exercise, or both and physical function in obese older adults. New England Journal of Medicine
364(13):1218–1229, 2011.CrossRefGoogle Scholar
Foster Schubert, K. E., et al., Effect of Diet and Exercise, Alone or Combined, on Weight and Body Composition in Overweight‐to‐Obese Postmenopausal Women. Obesity
20(8):1628–1638, 2012.CrossRefGoogle Scholar
Forster, M., et al., Cost-effectiveness of diet and exercise interventions to reduce overweight and obesity. International Journal of Obesity
35(8):1071–1078, 2011.CrossRefGoogle Scholar
Consortium, I., Validity of a short questionnaire to assess physical activity in 10 European countries. European journal of epidemiology
27(1):15, 2012.CrossRefGoogle Scholar
Wareham, N. J., et al., Validity and repeatability of a simple index derived from the short physical activity questionnaire used in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Public health nutrition
6(04):407–413, 2003.CrossRefMathSciNetGoogle Scholar
Wen, C. P., et al., Minimum amount of physical activity for reduced mortality and extended life expectancy: a prospective cohort study. The Lancet
378(9798):1244–1253, 2011.CrossRefGoogle Scholar
Corder, K., et al., Assessment of physical activity in youth. Journal of Applied Physiology
105(3):977–987, 2008.CrossRefGoogle Scholar
Tudor-Locke, C., et al., Utility of pedometers for assessing physical activity. Sports Medicine
32(12):795–808, 2002.CrossRefGoogle Scholar
Schneider, P. L., Crouter, S. E., and Bassett, D. R., Pedometer measures of free-living physical activity: comparison of 13 models. Medicine and Science in Sports and Exercise
36(2):331–335, 2004.CrossRefGoogle Scholar
Chen, K. Y., and Bassett, D. R., The technology of accelerometry-based activity monitors: current and future. Medicine and science in sports and exercise
37(11):S490, 2005.CrossRefGoogle Scholar
Ibata, Y., et al. Measurement of three-dimensional posture and trajectory of lower body during standing long jumping utilizing body-mounted sensors. in Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. 2013. IEEE.
Ohtaki, Y., et al., Recognition of daily ambulatory movements utilizing accelerometer and barometer. Power
100:102, 2004.Google Scholar
Khan, M., et al. A feature extraction method for realtime human activity recognition on cell phones. in Proceedings of 3rd International Symposium on Quality of Life Technology (isQoLT 2011). Toronto, Canada. 2011
Mathie, M. J., et al., Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiological measurement
25(2):R1, 2004.CrossRefGoogle Scholar
Parkka, J., et al., Activity classification using realistic data from wearable sensors. Information Technology in Biomedicine, IEEE Transactions on
10(1):119–128, 2006.CrossRefGoogle Scholar
Mathie, M., et al., Classification of basic daily movements using a triaxial accelerometer. Medical and Biological Engineering and Computing
42(5):679–687, 2004.CrossRefGoogle Scholar
Sekine, M., et al., Classification of waist-acceleration signals in a continuous walking record. Medical engineering & physics
22(4):285–291, 2000.CrossRefGoogle Scholar
Bao, L. and S.S. Intille, Activity recognition from user-annotated acceleration data, in Pervasive computing. 2004, Springer. p. 1–17.
Maurer, U., et al. Activity recognition and monitoring using multiple sensors on different body positions. in Wearable and Implantable Body Sensor Networks, 2006. BSN 2006. International Workshop on. 2006. IEEE.
Ermes, M., et al., Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. Information Technology in Biomedicine, IEEE Transactions on
12(1):20–26, 2008.CrossRefGoogle Scholar
Lee, Y.-S. and S.-B. Cho, Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer, in Hybrid Artificial Intelligent Systems. 2011, Springer. p. 460–467.
Bonomi, A. G., et al., Detection of type, duration, and intensity of physical activity using an accelerometer. Med Sci Sports Exerc
41(9):1770–1777, 2009.CrossRefGoogle Scholar
Allen, F. R., et al., Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models. Physiological Measurement
27(10):935, 2006.CrossRefGoogle Scholar
Karantonis, D. M., et al., Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. Information Technology in Biomedicine, IEEE Transactions on
10(1):156–167, 2006.CrossRefGoogle Scholar
Jin, G., Lee, S., and Lee, T., Context Awareness of Human Motion States Using Accelerometer. Journal of Medical Systems
32(2):93–100, 2008.CrossRefGoogle Scholar
Lee, J.-A., et al., Portable Activity Monitoring System for Temporal Parameters of Gait Cycles. Journal of Medical Systems
34(5):959–966, 2010.CrossRefGoogle Scholar
Yu, M., et al., Development of Abnormal Gait Detection and Vibratory Stimulation System on Lower Limbs to Improve Gait Stability. Journal of Medical Systems
34(5):787–797, 2010.CrossRefGoogle Scholar
Wu, W., et al., Classification accuracies of physical activities using smartphone motion sensors. Journal of medical Internet research, 2012. 14 (5).
Anguita, D., et al., Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine, in Ambient Assisted Living and Home Care. 2012, Springer. p. 216–223.
Kaghyan, S. and H. Sarukhanyan, Activity Recognition Using K-Nearest Neighbor Algorithm on Smartphone with Tri-axial Accelerometer. International Journal of Informatics Models and Analysis (IJIMA), ITHEA International Scientific Society, Bulgaria, 2012: p. 146–156
Mitchell, E., Monaghan, D., and O’Connor, N. E., Classification of sporting activities using smartphone accelerometers. Sensors
13(4):5317–5337, 2013.CrossRefGoogle Scholar
Kwapisz, J. R., Weiss, G. M., and Moore, S. A., Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter
12(2):74–82, 2011.CrossRefGoogle Scholar
Hamalainen, W., et al. Jerk-based feature extraction for robust activity recognition from acceleration data. in Intelligent Systems Design and Applications (ISDA), 2011 11th International Conference on. 2011. IEEE.
Hall, M.A., Correlation-based feature selection for machine learning. 1999, The University of Waikato.
Hall, M.A. and L.A. Smith. Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper. in FLAIRS Conference. 1999.
Garcıa López, F., et al., Solving feature subset selection problem by a parallel scatter search. European Journal of Operational Research, 2006. 169 (2): p. 477–489.
Marti, R., Laguna, M., and Glover, F., Principles of scatter search. European Journal of Operational Research
169(2):359–372, 2006.CrossRefMATHMathSciNetGoogle Scholar
Hall, M. A., and Holmes, G., Benchmarking attribute selection techniques for discrete class data mining. Knowledge and Data Engineering, IEEE Transactions on
15(6):1437–1447, 2003.CrossRefGoogle Scholar
Gutlein, M., et al. Large-scale attribute selection using wrappers. in Computational Intelligence and Data Mining, 2009. CIDM’09. IEEE Symposium on. 2009. IEEE.
Aha, D. W., Kibler, D., and Albert, M. K., Instance-based learning algorithms. Machine learning
6(1):37–66, 1991.Google Scholar
Breiman, L., Random forests. Machine learning
45(1):5–32, 2001.CrossRefMATHGoogle Scholar
Prasad, A. M., Iverson, L. R., and Liaw, A., Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems
9(2):181–199, 2006.CrossRefGoogle Scholar
Al-Allak, A., Bertelli, G., and Lewis, P., Random forests: The new generation of machine learning algorithms to predict survival in breast cancer. International Journal of Surgery
11(8):607–607, 2013.CrossRefGoogle Scholar
Biau, G., Analysis of a random forests model. The Journal of Machine Learning Research
98888(1):1063–1095, 2012.Google Scholar
Rodriguez, J. J., Kuncheva, L. I., and Alonso, C. J., Rotation forest: A new classifier ensemble method. Pattern Analysis and Machine Intelligence, IEEE Transactions on
28(10):1619–1630, 2006.CrossRefGoogle Scholar
Kuncheva, L.I. and J.J. Rodríguez, An experimental study on rotation forest ensembles, in Multiple Classifier Systems. 2007, Springer. p. 459–468.
Hall, M., et al., The WEKA data mining software: an update. ACM SIGKDD explorations newsletter
11(1):10–18, 2009.CrossRefGoogle Scholar
Lester, J., T. Choudhury, and G. Borriello, A practical approach to recognizing physical activities, in Pervasive Computing. 2006, Springer. p. 1–16.
Quinlan, J.R., C4. 5: programs for machine learning. Vol. 1. 1993: Morgan kaufmann.
Zhang, B., and Srihari, S. N., Fast k-nearest neighbor classification using cluster-based trees. Pattern Analysis and Machine Intelligence, IEEE Transactions on
26(4):525–528, 2004.CrossRefGoogle Scholar
Angiulli, F., Fast nearest neighbor condensation for large data sets classification. Knowledge and Data Engineering, IEEE Transactions on
19(11):1450–1464, 2007.CrossRefGoogle Scholar
Wilson, D. R., and Martinez, T. R., Reduction techniques for instance-based learning algorithms. Machine learning
38(3):257–286, 2000.CrossRefMATHGoogle Scholar
Arif, M., Malagore, I. A., and Afsar, F. A., Detection and localization of myocardial infarction using K-nearest neighbor classifier. Journal of medical systems
36(1):279–289, 2012.CrossRefGoogle Scholar
Sun, L., et al., Activity recognition on an accelerometer embedded mobile phone with varying positions and orientations, in Ubiquitous intelligence and computing. 2010, Springer. p. 548–562.
Allen, F.R., et al. An adapted gaussian mixture model approach to accelerometry-based movement classification using time-domain features. in Engineering in Medicine and Biology Society, 2006. EMBS’06. 28th Annual International Conference of the IEEE. 2006. IEEE.
Mi-hee Lee, J. K., Kwangsoo Kim
. Inho Lee, Sun Ha Jee, Sun Kook Yoo. Physical activity recognition using a single tri-axis accelerometer. in Proceedings of the world congress on engineering and computer science, 2009.Google Scholar
Kästner, M., et al. A Sparse Kernelized Matrix Learning Vector Quantization Model for Human Activity Recognition. in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2013.