uWave is a gesture recognizer based on a 3-D accelerometer. It was a project held by Rice Efficient Computing Group (RECG) of Rice University in collaboration with Motorola Labs in 2007/08. I became a research assistant of the group when I was an exchange student in Rice University and I took charge of the uWave project. We did not use Hidden Markov Model (HMM) popular in speech and some gesture recognitions as it requires a large amount of training samples, which often causes inconvenience in human-computer interaction. Dynamic Time Warping (DTW) algorithm and template adaptation were applied on the recognizer. It achieves 98.6% accuracy and only requires single training sample. It also allows users to employ personalized gestures. I was responsible for the design and development of the DTW algorithm on the gesture recognizer and part of the user study on the accuracy testing and improvement.
Conference and Journal Articles
- Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, and Venu Vasudevan, "uWave: Accelerometer-based personalized gesture recognition and its applications," in Pervasive and Mobile Computing, vol. 5, issue 6, pp. 657-675, December 2009. (Link)
- Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, and Venu Vasudevan, "User evaluation of lightweight user authentication with a single tri-axis accelerometer," in Proc. ACM Int. Conf. Human Computer Interaction with Mobile Devices and Services (MobileHCI), September 2009. (PDF)
- Jiayang Liu, Zhen Wang, Lin Zhong, Jehan Wickramasuriya, and Venu Vasudevan, "uWave: Accelerometer-based personalized gesture recognition and its applications," in IEEE Int. Conf. Pervasive Computing and Communication (PerCom), March 2009. (PDF, Demo) (Best Paper Award)
Extended Abstract and Technical Report
- Jiayang Liu, Zhen Wang, Lin Zhong, Jehan Wickramasuriya, and Venu Vasudevan, "uWave: Accelerometer-based personalized gesture recognition," Extended Abstract for demonstration in ACM Symposium on User Interface Software and Technology (UIST), October 2008. (PDF, Demo)
- Zhen Wang*, Jiayang Liu*, Lin Zhong, Jehan Wickramasuriya, and Venu Vasudevan, "uWave: Accelerometer-based personalized gesture recognition," Technical Report TR0630-08, Rice University and Motorola Labs, June 2008. (PDF) (*Equal contribution)
Acceleration data of 4480 gesture samples are collected from eight participants for seven days.
More details can be found in our PerCom paper.
Readme for the copyright & license, and organization of the library.
Download the library here.
Related Applications' Source CodeReadme for the copyright & license.
- uWave on Mac - The program on Mac collects data from Wii remote and does gesture recognition. Then it sends the result to TCP port so that any application that uses gesture recognition can listen to the port and react.
- Moto Q Media Player - uWave can be used to control the media player on Moto Q.
- Moto Q 3D Tvlicious - 3D interface for Tvlicious on Moto Q.
Demo Video on YouTube
You can also watch the demonstration on YouTube: