Identity authentication based on keystroke latencies
Communications of the ACM
Affective computing
Communicating emotions in online chat using physiological sensors and animated text
CHI '04 Extended Abstracts on Human Factors in Computing Systems
TypeTile: a keyboard system that decorates characters depending on the way of typing
SIGGRAPH '09: Posters
One-press control: a tactile input method for pressure-sensitive computer keyboards
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Sensor synaesthesia: touch in motion, and motion in touch
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Forcetap: extending the input vocabulary of mobile touch screens by adding tap gestures
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
GripSense: using built-in sensors to detect hand posture and pressure on commodity mobile phones
Proceedings of the 25th annual ACM symposium on User interface software and technology
VibPress: estimating pressure input using vibration absorption on mobile devices
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
| Hi-index | 0.00 |
In this paper, we propose a new way for measuring key typing pressure when using off-the-shelf laptop computers. Accelerometers embedded in laptop computers to protect hard discs from sudden motion are becoming very common. This paper explores the concept of utilizing this accelerometer for sensing non-verbal aspects of key typing, such as key typing pressure. This possibility enables a wide variety of pressure-sensitive user interfaces through the use of software without requiring any additional hardware/sensors. Such software can be distributed easily to a substantial number of potential users. To confirm the feasibility of this idea, we compared typing finger velocities (obtained by high-speed camera images) with sensor data from an accelerometer embedded in a laptop computer. We then confirmed that there is a clear correlation between these two sets of data. We also investigated differences in typing pressure patterns among different users. By combining keystroke speeds and typing pressure, we found it is possible to distinguish among users. This feature can be used for security purposes such as preventing a laptop computer from being used by non-owners. We also present possible application ideas such as rich text expression, new types of user interface elements, and authentication.