Multi-Sensor Ensemble Classifier for Activity Recognition
Affiliation(s)
School of Instrument Science and Engineering, Southeast University, Nanjing, China. Department of Mechanical Engineering, University of Connecticut, Storrs, CT, USA. Department of Kinesiology, University of Massachusetts, Amherst, MA, USA.
School of Instrument Science and Engineering, Southeast University, Nanjing, China. Department of Mechanical Engineering, University of Connecticut, Storrs, CT, USA. Department of Kinesiology, University of Massachusetts, Amherst, MA, USA.
ABSTRACT
This paper presents a multi-sensor ensemble classifier (MSEC) for physical activity (PA) pattern recognition of human subjects. The MSEC, developed for a wearable multi-sensor integrate measurement system (IMS),combines multiple classifiers based on different sensor feature sets to improve the accuracy of PA type identification.Experimental evaluation of 56 subjects has shown that the MSECis more effectivein assessing activities of varying intensitiesthan the traditional homogeneous classifiers. It is able to correctly recognize 6 PA types with an accuracy of 93.50%, which is 7% higher than the non-ensemble support vector machine method. Furthermore, the MSECis effective in reducing the subject-to-subject variabilityin activity recognition.
This paper presents a multi-sensor ensemble classifier (MSEC) for physical activity (PA) pattern recognition of human subjects. The MSEC, developed for a wearable multi-sensor integrate measurement system (IMS),combines multiple classifiers based on different sensor feature sets to improve the accuracy of PA type identification.Experimental evaluation of 56 subjects has shown that the MSECis more effectivein assessing activities of varying intensitiesthan the traditional homogeneous classifiers. It is able to correctly recognize 6 PA types with an accuracy of 93.50%, which is 7% higher than the non-ensemble support vector machine method. Furthermore, the MSECis effective in reducing the subject-to-subject variabilityin activity recognition.
Cite this paper
L. Mo, S. Liu, R. Gao and P. Freedson, "Multi-Sensor Ensemble Classifier for Activity Recognition," Journal of Software Engineering and Applications, Vol. 5 No. 12, 2012, pp. 113-116. doi: 10.4236/jsea.2012.512B022.
L. Mo, S. Liu, R. Gao and P. Freedson, "Multi-Sensor Ensemble Classifier for Activity Recognition," Journal of Software Engineering and Applications, Vol. 5 No. 12, 2012, pp. 113-116. doi: 10.4236/jsea.2012.512B022.
References
[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.
[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.