Optimal Recognition Method of Human Activities Using Artificial Neural Networks

Oniga, Stefan; József, Sütő

doi:10.1515/msr-2015-0044

Abstract

The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.

References

[1] Yang, A.Y., Jafari, R., Sastry, S.S., Bajcsy, R. (2009). Distributed recognition of human actions using wearable motion sensor networks. Journal of Ambient Intelligence and Smart Environments, 1 (2), 103-115.10.3233/AIS-2009-0016
Search in Google Scholar
[2] Yang, A., Kuryloski, P., Bajcsy, R. (2009). WARD: A wearable action recognition database. In 27th Annual CHI Conference, 4-9 April 2009, Boston, MA.
Search in Google Scholar
[3] Lara, O.D., Labrador, M.A. (2013). A survey on human activity recognition using wearable sensors. Communications Surveys & Tutorials, 15 (3), 1192-1209.10.1109/SURV.2012.110112.00192
Search in Google Scholar
[4] Preece, S.J., Goulermas, J.Y., Kenney, L.P., Howard, D. (2009). A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Transactions on Biomedical Engineering, 56 (3), 871-879.10.1109/TBME.2008.200619019272902
Search in Google Scholar
[5] Maurer, U., Smailagic, A., Siewiorek, D.P., Deisher, M. (2006). Activity recognition and monitoring using multiple sensors on different body positions. In International Workshop on Wearable and Implantable Body Sensor Networks (BSN 2006), 3-5 April 2006, Cambridge, MA. IEEE, 113-119.10.21236/ADA534437
Search in Google Scholar
[6] Yang, J.Y., Wang, J.S., Chen, Y.P. (2008). Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers. Pattern Recognition Letters, 29 (16), 2213-2220.10.1016/j.patrec.2008.08.002
Search in Google Scholar
[7] Gao, L., Bourke, A.K., Nelson, J. (2011). A system for activity recognition using multi-sensor fusion. In Annual International Conference of the IEEE - Engineering in Medicine and Biology Society (EMBC 2011), Boston, MA. IEEE, 7869-7872.
Search in Google Scholar
[8] Kouris, I., Koutsouris, D. (2013). Application of data mining techniques to efficiently monitor chronic diseases using wireless body area networks and smartphones. Universal Journal of Biomedical Engineering, 1 (2), 23-31.10.13189/ujbe.2013.010201
Search in Google Scholar
[9] Kouris, I., Koutsouris, D. (2011). A comparative study of pattern recognition classifiers to predict physical activities using smartphones and wearable body sensors. Technology and Health Care, 20 (4), 263-275.
Search in Google Scholar
[10] Gao, L., Bourke, A.K., Nelson, J. (2014). Evaluation of accelerometer based multi-sensor versus singlesensor activity recognition systems. Medical Engineering & Physics, 36 (6), 779-785.10.1016/j.medengphy.2014.02.01224636448
Search in Google Scholar
[11] Oniga, S., Suto, J. (2014). Human activity recognition using neural networks. In 15th International Carpathian Control Conference (ICCC 2014), 28-30 May 2014, Czech Republic. IEEE, 403-406.10.1109/CarpathianCC.2014.6843636
Search in Google Scholar
[12] Orha, I., Oniga, S. (2014). Study regarding the optimal sensors placement on the body for human activity recognition. In IEEE 20th International Symposium for Design and Technology in Electronic Packaging (SIITME 2014), 23-26 October 2014. IEEE, 203-206. 10.1109/SIITME.2014.6967028
Search in Google Scholar

Optimal Recognition Method of Human Activities Using Artificial Neural Networks

Abstract

Paradigm

My account