摘要:FingerLite:基于环境光的手指手势识别

Miao Huang, Haihan Duan, Yanru Chen, Yanbing Yang, J. Hao, Liangyin Chen
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引用次数: 2

摘要

随着物联网(IoT)的出现,与设备的自由交互是一个有前途的趋势。无调制环境光是一种令人兴奋的相互作用模态,但在可见光传感领域的研究和实践中仍存在不足,大多数研究都是基于调制光的解决方案。本文提出了一种低成本的基于环境光的实时手指手势识别系统。该系统依赖于循环神经网络(RNN)架构,无需复杂的预处理算法来完成手势分类任务。实验评估结果证明,我们提出的解决方案在特定用户群上的传感器布局具有较高的识别精度。
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Demo Abstract: FingerLite: Finger Gesture Recognition Using Ambient Light
Free hand interaction with devices is a promising trend with the advent of Internet of Things (IoT). The unmodulated ambient light, which can be an exciting modality for interaction, is still deficient in research and practice when most of the efforts in the field of visible light sensing are put into solutions based on modulated light. In this paper, we propose a low-cost ambient light-based system which performs finger gesture recognition in real-time. The system relies on a recurrent neural network (RNN) architecture without complicated pre-processing algorithms for the gesture classification task. The results of experimental evaluation proves that the solution that we put forward achieves a rather high recognition accuracy with our proposed sensor layout across a certain group of users.
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