Name
Abstract | ||
|---|---|---|
In this paper we present our work on real-time human gesture recognition for multimedia interactive controllers through the use of Microelectromechanical Systems (MEMS) 3 axes acceleration sensors. The changes of accelerations in three perpendicular directions due to different gesture motions are detected in real-time by 3-axes MEMS accelerometer embedded in a wireless micro sensing mote, which exports sensor data to a PC via Bluetooth protocol. In the data collection stage, in order to realize real-time recognition, an ldquoauto-cutrdquo algorithm was developed to gather the start and stop motions of an input gesture automatically. After comparing several different data processing methods, we chose Discrete Cosine Transforms (DCT) to reduce the dimension of the input gestures. Subsequently, a series of experiments were performed to analyze the influence of sensor sampling frequency and the number of dominant frequencies for various gestures, and then the best combination was selected for our recognition experiments. Finally, the Hidden Markov Model (HMM) was employed to achieve real-time gesture recognition. We have shown that the gesture recognition accuracy could reach 95.7% when 20 training samples of each gesture and 70 testing samples were used. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/NEMS.2009.5068728 | NEMS |
Keywords | Field | DocType |
real-time recognition,different data,micromechanical devices,input gesture,gesture recognition accuracy,real-time gesture recognition,recognition experiment,data collection stage,various gesture,mems motion sensors,interactive controller,real-time human gesture recognition,discrete cosine transforms,different gesture motion,3 axes acceleration sensors,gesture recognition,microelectromechanical systems,mems accelerometer,hidden markove model,hidden markov models,mems motion sensor,multimedia interactive controllers,hidden markov model,accuracy,time frequency analysis,databases,sampling frequency,real time,sensors,discrete cosine transform,data processing,data collection | Computer vision,Data processing,Composite material,Gesture,Accelerometer,Discrete cosine transform,Gesture recognition,Acceleration,Artificial intelligence,Hidden Markov model,Materials science,Bluetooth | Conference |
ISSN | ISBN | Citations |
2474-3747 | 978-1-4244-4630-8 | 6 |
PageRank | References | Authors |
0.59 | 3 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengli Zhou | 1 | 3909 | 262.81 |
| Qing Shan | 2 | 25 | 2.67 |
| Fei Fei | 3 | 11 | 3.03 |
| Wenjung Li | 4 | 171 | 49.26 |
| Chung Ping Kwong | 5 | 19 | 2.64 |
| Patrick C. K. Wu | 6 | 6 | 0.59 |
| Bojun Meng | 7 | 25 | 4.75 |
| Christina K. H. Chan | 8 | 6 | 0.59 |
| Jay Y. J. Liou | 9 | 6 | 0.59 |