Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 106, Taiwan.
NVIDIA Corp., 11001 Lakeline Blvd #100, Austin, TX 78717, USA.
Sensors (Basel). 2019 Sep 15;19(18):3986. doi: 10.3390/s19183986.
The goal of this work is to present a novel continuous finger gesture recognition system based on flex sensors. The system is able to carry out accurate recognition of a sequence of gestures. Wireless smart gloves equipped with flex sensors were implemented for the collection of the training and testing sets. Given the sensory data acquired from the smart gloves, the gated recurrent unit (GRU) algorithm was then adopted for gesture spotting. During the training process for the GRU, the movements associated with different fingers and the transitions between two successive gestures were taken into consideration. On the basis of the gesture spotting results, the maximum a posteriori (MAP) estimation was carried out for the final gesture classification. Because of the effectiveness of the proposed spotting scheme, accurate gesture recognition was achieved even for complicated transitions between successive gestures. From the experimental results, it can be observed that the proposed system is an effective alternative for robust recognition of a sequence of finger gestures.
这项工作的目标是提出一种基于挠曲传感器的新型连续手指手势识别系统。该系统能够准确识别一系列手势。为了收集训练集和测试集，我们实现了配备挠曲传感器的无线智能手套。对于从智能手套采集到的传感数据，我们采用门控循环单元（GRU）算法进行手势识别。在 GRU 的训练过程中，我们考虑了不同手指的运动和两个连续手势之间的转换。基于手势识别结果，我们进行了最大后验（MAP）估计，以进行最终的手势分类。由于所提出的识别方案的有效性，即使对于连续手势之间复杂的转换，也能实现准确的手势识别。从实验结果可以看出，所提出的系统是一种用于稳健识别连续手指手势的有效替代方案。
