<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

Optimal Recognition Method of Human Activities Using Artificial Neural Networks

Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Technical University of Cluj-Napoca, North University Center of Baia Mare, Dr. V. Babes Street, No. 62A, ZIP 430083, Baia Mare, Romania

Department of Informatics Systems and Networks, Faculty of Informatics, University of Debrecen, Egyetem ter, 1., 4032, Debrecen, Hungary

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Optimal Recognition Method of Human Activities Using Artificial Neural Networks"}

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...

Optimal Recognition Method of Human Activities Using Artificial Neural Networks

Pubblicato online: 30 dic 2015

Pagine: 323 - 327

Ricevuto: 31 lug 2015

Accettato: 02 dic 2015

DOI: https://doi.org/10.1515/msr-2015-0044

Parole chiave
Wearable sensor network, artificial neural networks, human activity recognition, pattern recognition

© by Stefan Oniga

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Optimal Recognition Method of Human Activities Using Artificial Neural Networks

Pubblicato online: 30 dic 2015

Pagine: 323 - 327

Ricevuto: 31 lug 2015

Accettato: 02 dic 2015

DOI: https://doi.org/10.1515/msr-2015-0044

Parole chiaveWearable sensor network, artificial neural networks, human activity recognition, pattern recognition

© by Stefan Oniga

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
Wearable sensor network, artificial neural networks, human activity recognition, pattern recognition