Abstract
This paper describes an experiment to validate whether unconscious responses or conscious gazing motions to notification tones can be discriminated from skin conductance responses or electro-oculograms. Our goal is to solve a problem that a smartphone cannot discriminate that a user has noticed a notification from the smartphone unless the user directly operates it or speaks to it when the user noticed the notification. In our experiment, participants were presented with notification tones while they were watching a video or reading orally as a main task, and their physiological signals were recorded during the task. As the results, we found that it took approximately four seconds to discriminate the response from skin conductance responses, whereas it took only one second to discriminate the response from the electro-oculogram. Furthermore, we found that the recall was 92.5% and the precision was 96.1% for discriminating the conscious gazing motions to the notification tones from the electro-oculograms between upper and lower of an eye.
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References
Andersen, H. J., Morrison, A., Knudsen, L.: Modeling vibrotactile detection by logistic regression. In: Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design (NordiCHI 2012), New York, NY, USA, pp. 500–503. Association for Computing Machinery (2012). https://doi.org/10.1145/2399016.2399092
Blum, J.R., Frissen, I., Cooperstock, J.R.: Improving haptic feedback on wearable devices through accelerometer measurements. In: Proceedings of the 28th Annual ACM symposium on user interface software & Technology (UIST 2015), New York, NY, USA, 31–36. Association for Computing Machinery (2015). https://doi.org/10.1145/2807442.2807474
Fortin, P.E., Sulmont, E., Cooperstock, J.: Detecting perception of smartphone notifications using skin conductance responses. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI 2019), New York, NY, USA, Paper 190, 1–9. Association for Computing Machinery (2019). https://doi.org/10.1145/3290605.3300420
Poppinga, B., Heuten, W., Boll, S.: Sensor-based identification of opportune moments for triggering notifications. IEEE Pervasive Comput. 13(1), 22–29 (2014). https://doi.org/10.1109/MPRV.2014.15
Yao, H., Grant, D., Cruz, M.: Perceived vibration strength in mobile devices: the effect of weight and frequency. IEEE Trans. Haptics 3(1), 56–62 (2010). https://doi.org/10.1109/TOH.2009.37
Miyata, Y. (ed.).: New Physiological psychology. Kitaohji Shobo, p328, Kyoto, (1998)
Hori T.: Physiological psychology. Baifukan, p. 164 (2008)
Widmann, A., Engbert, R., Schröger, E.: Microsaccadic responses indicate fast categorization of sounds: a novel approach to study auditory cognition. J. Neurosci. 34(33), 11152–11158 (2014). https://doi.org/10.1523/JNEUROSCI.1568-14
Yamanaka, Y.: Objective Hearing Test using Eye Movements [Translated from Japanese.]. Practica Oto-Rhino-Laryngologica 58(6), pp. 313–319 (1965). https://doi.org/10.5631/jibirin.58.313
Ono, Y.: Non-invasive biosignal processing and analysis IV, electromyogram and electro-oculogram: measurement and signal analysis for bioengineering. Contents Syst. Control Inf. 62(8), 337–342 (2018)
Bulling, A., Ward, J.A., Gellersen, H., Tröster, G.: Eye movement analysis for activity recognition using electrooculography. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 33(4), 741–753 (2011)
Benedek, M., Kaernbach, C.: A continuous measure of phasic electrodermal activity. J. Neurosci. Methods 190(1), 80–91 (2010). https://doi.org/10.1016/j.jneumeth.2010.04.028
JINS. JINS MEME. https://jinsmeme.com/en. Accessed 21 Jun 2023
Usui, T., Ban, Yamamoto, T.: A study on estimating eye direction using smart eyewear. In: Proceedings of Multimedia, Distributed, Cooperative, and Mobile Symposium 2016, pp. 1172–1174 (2016)
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Omata, M., Ito, S. (2023). Electro-oculographic Discrimination of Gazing Motion to a Smartphone Notification Tone. In: da Silva, H.P., Cipresso, P. (eds) Computer-Human Interaction Research and Applications. CHIRA 2023. Communications in Computer and Information Science, vol 1996. Springer, Cham. https://doi.org/10.1007/978-3-031-49425-3_11
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