Abstract
Touch screens are nowadays one of the major interfaces in the interaction between humans and technology, mostly due to the significant growth in the use of smartphones and tablets in the last years. This broad use, that reaches people from all strata of society, makes touch screens a relevant tool to study the mechanisms that influence the way we interact with electronic devices. In this paper we collect data regarding the interaction patterns of different users with mobile devices. We present a way to formalize these interaction patterns and analyze how aspects such as age and gender influence them. The results of this research may be relevant for developing mobile applications that identify and adapt to the users or their characteristics, including impairments in fine motor skills or in cognitive function.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Oulasvirta, A., Rattenbury, T., Ma, L., Raita, E.: Habits make smartphone use more pervasive. Pers. Ubiquit. Comput. 16(1), 105–114 (2012)
Leeming, K., Swann, W., Coupe, J., Mittler, P.: Non-verbal communication. In: Teaching Language and Communication to the Mentally Handicapped, pp. 238–267, Routledge (2018)
Carneiro, D., Novais, P., Pêgo, J.M., Sousa, N., Neves, J.: Using mouse dynamics to assess stress during online exams. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds.) HAIS 2015. LNCS (LNAI), vol. 9121, pp. 345–356. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19644-2_29
Pimenta, A., Carneiro, D., Neves, J., Novais, P.: A neural network to classify fatigue from human-computer interaction. Neurocomputing 172, 413–426 (2016)
Pentel, A.: Predicting age and gender by keystroke dynamics and mouse patterns. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 381–385. ACM (2017)
Nahin, A.N.H., Alam, J.M., Mahmud, H., Hasan, K.: Identifying emotion by keystroke dynamics and text pattern analysis. Behav. Inf. Technol. 33(9), 987–996 (2014)
Ciman, M., Wac, K.: Individuals’ stress assessment using human-smartphone interaction analysis. IEEE Trans. Affect. Comput. 9(1), 51–65 (2018)
Mehrotra, A., Hendley, R., Musolesi, M.: Towards multi-modal anticipatory monitoring of depressive states through the analysis of human-smartphone interaction. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pp. 1132–1138. ACM (2016)
Padmaja, B., Prasad, V.R., Sunitha, K.: TreeNet analysis of human stress behavior using socio-mobile data. J. Big Data 3(1), 24 (2016)
Boonstra, T.W., Nicholas, J., Wong, Q.J., Shaw, F., Townsend, S., Christensen, H.: Using mobile phone sensor technology for mental health research: integrated analysis to identify hidden challenges and potential solutions. J. Med. Internet Res. 20(7), e10131 (2018)
Sanchis, A., Julián, V., Corchado, J.M., Billhardt, H., Carrascosa, C.: Using natural interfaces for human-agent immersion. In: Corchado, J.M., Bajo, J., Kozlak, J., Pawlewski, P., Molina, J.M., Gaudou, B., Julian, V., Unland, R., Lopes, F., Hallenborg, K., García Teodoro, P. (eds.) PAAMS 2014. CCIS, vol. 430, pp. 358–367. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07767-3_32
Sanchis, Á., Inglada, J., Javier, V., Corchado, J.M., Billhardt, H., Carrascosa Casamayor, C.: Improving human-agent immersion using natural interfaces and CBR. Int. J. Artif. Intell. 13(1), 81–93 (2015)
Acknowledgments
This work is co-funded by Fundos Europeus Estruturais e de Investimento (FEEI) through Programa Operacional Regional Norte, in the scope of project NORTE-01-0145-FEDER-023577.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rocha, R., Carneiro, D., Novais, P. (2019). The Influence of Age and Gender in the Interaction with Touch Screens. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-30244-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30243-6
Online ISBN: 978-3-030-30244-3
eBook Packages: Computer ScienceComputer Science (R0)