Abstract
Emerging sensor technology and mobile devices offer the possibility to capture users’ current state more easily. However, the registration of users’ biosignals is still a challenge because of the lack of user acceptance.
In this article, we focus on users’ perceptions about wearable devices for registering the electroencephalogram (EEG). We consider subjects’ rankings of the wearable devices and relate them to personal characteristics on the one hand and device properties on the other. Finally, we show for each device separately which evaluation criteria accounted for the given ranking.
Thereby, our results indicated that subjects’ preference is influenced the most by the properties of the devices and to a lesser extent by the personal characteristics of the user. The results can contribute to the understanding of users’ device preference and advice developers of wearable technology regarding users’ needs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Knight, J.F., Deen-Williams, D., Arvanitis, T.N., Baber, C., Sotiriou, S., Anastasopoulou, S., Gargalakos, M.: Assessing the wearability of wearable computers. In: 10th IEEE International Symposium on Wearable Computers, pp. 75–82. IEEE (2006)
Wright, R., Keith, L.: Wearable technology: if the tech fits, wear it. J. Electron. Resour. Med. Libr. 11(4), 204–216 (2014)
Rogers, J., Johnstone, S., Aminov, A., Donnelly, J., Wilson, P.H.: Test-retest reliability of a single-channel, wireless EEG system. Int. J. Psychophysiol. 106, 87–96 (2016)
Callan, D.E., Durantin, G., Terzibas, C.: Classification of single-trial auditory events using dry-wireless EEG during real and motion simulated flight. Front. Syst. Neurosci. 9(11), (2015)
Ekandem, J., Davis, T., Alvarez, I., James, M., Gilbert, J.: Evaluating the ergonomics of BCI devices for research and experimentation. Ergonomics 5, 592–598 (2012)
Hairston, W., Whitaker, K., Ries, A., Vettel, J., Bradford, J., Kerick, S., McDowell, K.: Usability of four commercially-oriented EEG systems. J. Neural Eng. 11(4), 046018 (2014)
Reed, K., Doty, H.D., May, D.R.: The impact of aging on self-efficacy and computer skill acquisition. J. Manag. Issues 17(2), 212–228 (2005)
Marquie, J.C., Jourdan-Boddaert, L., Huet, N.: Do older adults underestimate their actual computer knowledge? Behav Inf. Technol. 21(4), 273–280 (2002)
Gaul, S., Ziefle, M.: Smart home technologies: insights into generation-specific acceptance motives. In: Holzinger, A., Miesenberger, K. (eds.) HCI and Usability for e-Inclusion, USAB 2009. LNCS, vol. 5889, pp. 312–332. Springer, Heidelberg (2009)
Baldi, R.A.: Training older adults to use the computer: issues related to the workplace. Attitudes Train. Educ. Gerontol. 23, 453–465 (1997)
Busch, T.: Gender differences in self-efficacy and attitudes towards computers. J. Educ. Comput. Res. 12, 147–158 (1995)
Schumacher, P., Morahan-Martin, J.: Gender, internet and computer attitudes and experiences. Comput. Hum. Behav. 17, 95–110 (2001)
Wasinger, R., Krüger, A.: Modality preferences in mobile and instrumented environments. In: Proceedings of the 11th International Conference on Intelligent User Interfaces, IUI 2006, New York, USA, pp. 336–338 (2006)
Schüssel, F., Honold, F., Weber, M.J.: Influencing Factors on Multimodal Interaction at Selection Tasks. Open Access Repositorium der Universität Ulm. http://dx.doi.org/10.18725/OPARU-2425
De Angeli, A., Gerbino, W., Cassano, G., Petrelli, D.: Visual display, pointing, and natural language: the power of multimodal interaction. In: Proceedings of the Working Conference on Advanced Visual Interfaces, AVI 1998, pp. 164–173. New York, USA (1998)
Reis, T., de Sá, M., Carriço, L.: Multimodal interaction: real context studies on mobile digital artefacts. In: Pirhonen, A., Brewster, S. (eds.) Haptic and Audio Interaction Design. HAID 2008, LNCS, vol. 5270, pp. 60–69. Springer, Berlin, Heidelberg (2008)
Nitsch, V., Glassen, T.: Investigating the effects of robot behavior and attitude towards technology on social human-robot interactions. In: 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Kobe, pp. 535–540 (2015)
Mertens, A., Wille, M., Theis, S., Rasche, P., Finken, L., Schlick, C.M.: Attitudes of elderly people towards assistive system: influence of amortization barriers on the adherence in technically assisted rehabilitation and the diffusion of health technologies. In: Proceedings of 19th Triennial Congress of the IEA, Melbourne (2015)
Karrer, K., Glaser, C., Clemens, C., Bruder, C.: Technikaffinität erfassen – der Fragebogen TA-EG. In: Lichtenstein, A., Stößel, C., Clemens, C. (eds.) Der Mensch im Mittelpunkt technischer Systeme. 8. Berliner Werkstatt Mensch-Maschine-Systeme, ZMMS Spektrum, vol. 22, pp. 196–201. VDI Verlag GmbH, Düsseldorf (2009)
Acknowledgments
We would like to thank Friederice Schröder for conducting the experiments and my student assistants Emilia Cheladze and Lea Rabe for daily operational and computational support. We would also like to thank Gabriele Freude for her general project support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Radüntz, T., Rose, U. (2018). Influence of Personal Characteristics and Device Properties on Wearable’s Rank Order. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration. IHSI 2018. Advances in Intelligent Systems and Computing, vol 722. Springer, Cham. https://doi.org/10.1007/978-3-319-73888-8_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-73888-8_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73887-1
Online ISBN: 978-3-319-73888-8
eBook Packages: EngineeringEngineering (R0)