Abstract
In recent years, gesture-based interaction gained increasing interest in Ambient Intelligence. Especially the success of camera-based gesture recognition systems shows that a great variety of applications can benefit significantly from natural and intuitive interaction paradigms. Besides camera-based systems, proximity-sensing surfaces are especially suitable as an input modality for intelligent environments. They can be installed ubiquitously under any kind of non-conductive surface, such as a table. However, interaction barriers and the types of supported gestures are often not apparent to the user. In order to solve this problem, we investigate an approach which combines a semi-transparent capacitive proximity-sensing surface with an LED array. The LED array is used to indicate possible gestural movements and provide visual feedback on the current interaction status. A user study shows that our approach can enhance the user experience, especially for inexperienced users.
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Ballagas, R., Borchers, J., Rohs, M., Sheridan, J.G.: The smart phone: A ubiquitous input device. IEEE Pervasive Computing 5(1), 70–77 (2006)
Braun, A., Hamisu, P.: Using the human body field as a medium for natural interaction. In: PETRA 2009, pp. 50:1–50:7 (2009)
Cohn, G., Morris, D., Patel, S., Tan, D.: Humantenna: Using the body as an antenna for real-time whole-body interaction. In: CHI 2012, pp. 1901–1910 (2012)
Glinsky, A.: Theremin: Ether Music and Espionage. University of Illinois Press (2000)
Grosse-Puppendahl, T., Berghoefer, Y., Braun, A., Wimmer, R., Kuijper, A.: Opencapsense: A rapid prototyping toolkit for pervasive interaction using capacitive sensing. In: PerCom 2013, pp. 152–159 (2013)
Grosse-Puppendahl, T., Braun, A., Kamieth, F., Kuijper, A.: Swiss-cheese extended: An object recognition method for ubiquitous interfaces based on capacitive proximity sensing. In: CHI 2013, pp. 1401–1410 (2013)
Harrison, C., Sato, M., Poupyrev, I.: Capacitive fingerprinting: Exploring user differentiation by sensing electrical properties of the human body. In: UIST 2012, pp. 537–544 (2012)
Majewski, M., Braun, A., Marinc, A., Kuijper, A.: Providing visual support for selecting reactive elements in intelligent environments. In: Gavrilova, M.L., Tan, C.J.K., Kuijper, A. (eds.) Transactions on Computational Science XVIII. LNCS, vol. 7848, pp. 248–263. Springer, Heidelberg (2013)
Microsoft: http://www.xbox.com/kinect/ (accessed June 20, 2013)
Poupyrev, I., Yeo, Z., Griffin, J.D., Hudson, S.: Sensing human activities with resonant tuning. In: CHI 2010 EA, pp. 4135–4140 (2010)
Sato, M., Poupyrev, I., Harrison, C.: Touché: Enhancing touch interaction on humans, screens, liquids, and everyday objects. In: CHI 2012, pp. 483–492 (2012)
Smith, J.R., Gershenfeld, N., Benton, S.A.: Electric Field Imaging. Ph.D. thesis, Massachusetts Institute of Technology (1999)
Sodhi, R., Benko, H., Wilson, A.: Lightguide: Projected visualizations for hand movement guidance. In: CHI 2012, pp. 179–188 (2012)
Sousa, M., Techmer, A., Steinhage, A., Lauterbach, C., Lukowicz, P.: Human tracking and identification using a sensitive floor and wearable accelerometers. In: PerCom 2013, vol. 18, p. 22 (2013)
Valtonen, M., Vuorela, T., Kaila, L., Vanhala, J.: Capacitive indoor positioning and contact sensing for activity recognition in smart homes. JAISE 4, 1–30 (2012)
Wimmer, R., Kranz, M., Boring, S., Schmidt, A.: Captable and capshelf - unobtrusive activity recognition using networked capacitive sensors. In: INSS 2007, pp. 85–88 (2007)
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Grosse-Puppendahl, T., Beck, S., Wilbers, D., Zeiß, S., von Wilmsdorff, J., Kuijper, A. (2014). Ambient Gesture-Recognizing Surfaces with Visual Feedback. In: Streitz, N., Markopoulos, P. (eds) Distributed, Ambient, and Pervasive Interactions. DAPI 2014. Lecture Notes in Computer Science, vol 8530. Springer, Cham. https://doi.org/10.1007/978-3-319-07788-8_10
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DOI: https://doi.org/10.1007/978-3-319-07788-8_10
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