Smartphone-Based Human Activity Recognition by Machine Learning Methods
Authors: Yanting Cao, Kazumitsu Nawata
As smartphones are continually upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described more refined, complex and detailed. In this context, we analyzed a set of experimental data, obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model become extremely challenging. After a series of feature selection and parameters adjustments, a well-performed SVM classifier has been trained.
Keywords: smart sensors, human activity recognition, artificial intelligence, SVMProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 464
 Wang, J., Chen, Y., Hao, S., Peng, X., and Hu, L., “Deep learning for sensor-based activity recognition: A survey”. Pattern Recognition Letters119, 2019, pp3-11.
 Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J. L., “A public domain dataset for human activity recognition using smartphones”. In Esann, 2013, Vol. 3, pp3.
 Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J. L., “Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine”. In International workshop on ambient assisted living, Springer, Berlin, Heidelberg, 2012, pp. 216-223.
 Yang, J. Y., Wang, J. S., and Chen, Y. P., “Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers”. Pattern recognition letters 29(16), 2018, pp2213-2220.
 Khan, A. M., Lee, Y. K., Lee, S. Y., and Kim, T. S., “Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis”. In 2010 5th international conference on future information technology. IEEE, 2010, pp1-6.
 Granitto, P. M., Furlanello, C., Biasioli, F., and Gasperi, F., “Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products”. Chemometrics and intelligent laboratory systems, 2006, 83(2), pp83-90.
 Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., “Gene selection for cancer classification using support vector machines”, Mach. Learn.,2002, 46(1-3), pp.389–422.
 Frank, A., and Asuncion, A., “Human Activity Recognition Using Smartphones Data Set”. UCI machine learning repository, 2010.
 Cover, T., and Hart, P., “Nearest neighbor pattern classification”. IEEE transactions on information theory, 1967, 13(1), pp. 21-27.
 Zhou Z H., “Ensemble methods: foundations and algorithms
[M]”. Chapman and Hall/CRC, 2019.
 Cortes, C., and Vapnik, V., “Support-vector networks”. Machine learning,1995, 20(3), pp273-297.
 Mantovani, R. G., Rossi, A. L., Vanschoren, J., Bischl, B., and Carvalho, A. C., “To tune or not to tune: recommending when to adjust SVM hyper-parameters via meta-learning”. In 2015 International Joint Conference on Neural Networks (IJCNN), Ieee, 2015, pp1-8.
 Subasi, Abdulhamit, et al., “Smartphone-based human activity recognition using bagging and boosting”. Procedia Computer Science 163, 2019, pp54-61.
 Hesterman, J. Y., Caucci, L., Kupinski, M. A., Barrett, H. H., and Furenlid, L. R., “Maximum-likelihood estimation with a contracting-grid search algorithm”. IEEE transactions on nuclear science, 2010, 57(3), pp1077-1084.