Wenhui Wang, Xiang Chen, Kongqiao Wang, Xu Zhang, Jihai Yang
Institute of Biomedical Engineering at the University of Science and Technology of China, Hefei, China.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:7014-7. doi: 10.1109/IEMBS.2009.5333326.
This paper investigates the roles of a three-axis accelerometer, surface electromyography sensors and a webcam for dynamic gesture recognition. A decision-level multiple sensor fusion method based on action elements is proposed to distinguish a set of 20 kinds of dynamic hand gestures. Experiments are designed and conducted to collect three kinds of sensor data stream simultaneously during gesture implementation and compare the performance of different subsets in gesture recognition. Experimental results from three subjects show that the combination of three kinds of sensor achieves recognition accuracies at 87.5%-91.8%, which are higher largely than that of the single sensor conditions. This study is valuable to realize continuous and dynamic gesture recognition based on multiple sensor fusion technology for multi-model interaction.
本文研究了三轴加速度计、表面肌电图传感器和网络摄像头在动态手势识别中的作用。提出了一种基于动作元素的决策级多传感器融合方法，以区分20种动态手势。设计并进行了实验，以便在手势执行过程中同时收集三种传感器数据流，并比较不同子集在手势识别中的性能。来自三名受试者的实验结果表明，三种传感器的组合实现了87.5%-91.8%的识别准确率，大大高于单传感器条件下的准确率。本研究对于基于多传感器融合技术实现用于多模式交互的连续动态手势识别具有重要价值。
