Smartphone-Based Human Activity Recognition by Machine Learning Methods

Authors: Yanting Cao, Kazumitsu Nawata

Abstract:

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, SVM

References:

[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[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.
[7] 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.
[8] Frank, A., and Asuncion, A., “Human Activity Recognition Using Smartphones Data Set”. UCI machine learning repository, 2010.
[9] Cover, T., and Hart, P., “Nearest neighbor pattern classification”. IEEE transactions on information theory, 1967, 13(1), pp. 21-27.
[10] Zhou Z H., “Ensemble methods: foundations and algorithms
[M]”. Chapman and Hall/CRC, 2019.
[11] Cortes, C., and Vapnik, V., “Support-vector networks”. Machine learning,1995, 20(3), pp273-297.
[12] 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.
[13] Subasi, Abdulhamit, et al., “Smartphone-based human activity recognition using bagging and boosting”. Procedia Computer Science 163, 2019, pp54-61.
[14] 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.