Augmented Touch is a project on touch behaviour. It included the effect of using touch positon feature and sensor features to analysis hand posture classification probelm and authentication user on mobile devices.

I wish you could be inspired by this repository to keep interested in develop a new way to use sensor information, such as adapt user interface on mobile devices by using sensor behaviour and touch behaviour. You can see a very breif demo on YouTube.

This project is inspired and coached from few touch behaviour research by Daniel Buschek, and you can find the related works here.

• About
• [Apply your own user study](Apply your own user study)
  • MotionGraphs
  • TouchMotion
  • [TouchMotion Collect](./src/TouchMotion Collect/README.md)
• Dataset
• Build & Run
• License

1. Alon, J., Sclaroff, S., Kollios, G., & Pavlovic, V. (2003). Discovering clusters in motion time-series data. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., 1, I–375.

• Buschek, D., & Alt, F. (2015). TouchML. In Proceedings of the 20th International Conference on Intelligent User Interfaces - IUI ’15 (pp. 110–114). New York, New York, USA: ACM Press.
• Buschek, D., De Luca, A., & Alt, F. (2015). Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI ’15 (pp. 1393–1402). New York, New York, USA: ACM Press.
• Buschek, D., Rogers, S., & Murray-Smith, R. (2013). User-specific touch models in a cross-device context. In Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services - MobileHCI ’13 (p. 382). New York, New York, USA: ACM Press.
• Buschek, D., Schoenleben, O., & Oulasvirta, A. (2014). Improving accuracy in back-of-device multitouch typing. In Proceedings of the 19th international conference on Intelligent User Interfaces - IUI ’14 (pp. 57–66). New York, New York, USA: ACM Press.
• Goel, M., Wobbrock, J. O., & Patel, S. N. (2012). GripSense: Using built-in sensors to detect hand posture and pressure on commodity mobile phones. In Proceedings of the 25th annual ACM symposium on User interface software and technology (pp. 545–554).
• Hinckley, K., & Song, H. (2011). Sensor Synaesthesia : Touch in Motion , and Motion in Touch. Human Factors, 801–810.
• Holz, C., & Baudisch, P. (2010). The Generalized Perceived Input Point Model and How to Double Touch Accuracy by Extracting Fingerprints. Proceedings of the 28th International Conference on Human Factors in Computing Systems - CHI ’10, 581–590.
• Krishnan, Narayanan; Cook, D. (2012). Activity Recognition on Streaming Sensor Data, 13(9), 1133–1145.
• McGrath, W., & Li, Y. (2014). Detecting Tapping Motion on the Side of Mobile Devices By Probabilistically Combining Hand Postures.In UIST 2014 (pp. 215–219).
• Negulescu, M., & McGrenere, J. (2015). Grip Change as an Information Side Channel for Mobile Touch Interaction. In CHI 2015 (pp. 1519–1522).
• Seipp, K., & Devlin, K. (2014). BackPat: One-Handed Off-Screen Patting Gestures. In MobileHCI 2014 (pp. 6–9).

• Changkun Ou

• Daniel Buschek

You can download datasets/results and run/modify the user study software, but you are forbidden to copy and republish it without any modify on condition of contributors anonymous.

Furthurmore, You need site the name of contributors in acknowledgement when you publish your studff.

© Contributors & Advisor, 2015-2016. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. All source code licensed under a GNU Public v2 License.