Accelerometer-based gesture control for a design environment
Personal and Ubiquitous Computing
Warping the time on data streams
Data & Knowledge Engineering
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
Proceedings of the 20th annual ACM symposium on User interface software and technology
Toward accurate dynamic time warping in linear time and space
Intelligent Data Analysis
Natural throw and tilt interaction between mobile phones and distant displays
CHI '09 Extended Abstracts on Human Factors in Computing Systems
Gesture Recognition with a 3-D Accelerometer
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
uWave: Accelerometer-based personalized gesture recognition and its applications
Pervasive and Mobile Computing
Accelerometer based gesture recognition using continuous HMMs
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
On the practicality of motion based keystroke inference attack
TRUST'12 Proceedings of the 5th international conference on Trust and Trustworthy Computing
Experiencing real 3D gestural interaction with mobile devices
Pattern Recognition Letters
Walk detection and step counting on unconstrained smartphones
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
| Hi-index | 0.00 |
Recently, several smart phones are equipped with a 3D-accelerometer that can be used for gesture-based user interface (UI). In order to utilize the gesture UI for the real-time systems with various users, the diversity robust algorithm, yet having low training/recognition complexity, is required. Meantime, dynamic time warping (DTW) has shown good performance on the simple time-series pattern recognition problems. Since DTW is based on the template matching, its processing time and accuracy depend on the number of templates and their quality, respectively. In this paper, an optimized method for online gesture UI of mobile devices is proposed which is based on the DTW and modified k-means clustering algorithm. The templates, estimated by using the modified clustering algorithm, can preserve the time varying attribute while contain diversities of the given training patterns. The proposed method was validated on 20 types of gestures which are designed for the mobile contents browsing. The experimental results showed that the proposed method is suitable to the online mobile UI.