Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Augmenting the mouse with pressure sensitive input
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Expressive typing: a new way to sense typing pressure and its applications
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Pressure-based text entry for mobile devices
Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services
GraspZoom: zooming and scrolling control model for single-handed mobile interaction
Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services
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
PseudoButton: enabling pressure-sensitive interaction by repurposing microphone on mobile device
CHI '12 Extended Abstracts on Human Factors in Computing Systems
MicPen: pressure-sensitive pen interaction using microphone with standard touchscreen
CHI '12 Extended Abstracts on Human Factors in Computing Systems
The fat thumb: using the thumb's contact size for single-handed mobile interaction
MobileHCI '12 Proceedings of the 14th 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
| Hi-index | 0.00 |
This paper introduces VibPress, a software technique that enables pressure input interaction on mobile devices by measuring the level of vibration absorption with the built-in accelerometer when the device is in contact with a damping surface (e.g., user's hands). This is achieved using a real-time estimation algorithm running on the device. Through a user evaluation, we provide evidence that this system is faster than previous software-based approaches, and accurate as hardware-augmented approaches (up to 99.7% accuracy). With this work, we also provide an insight about the maximum number of pressure levels that users can reliably distinguish, reporting usability metrics (time, errors and cognitive load) for different pressure levels and types of gripping gestures (press and squeeze).