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Design and evaluation of a hand gesture recognition approach for real-time interactions

机译：实时交互手势识别方法的设计与评估

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摘要

Hand gestures are a natural and intuitive form for human-environment interaction and can be used as an input alternative in human-computer interaction (HCI) to enhance usability and naturalness. Many existing approaches have employed vision -based systems to detect and recognize hand gestures. However, vision-based systems usually require users to move their hands within restricted space, where the optical device can capture the motion of hands. Also, vision-based systems may suffer from self-occlusion issues due to sophisticated finger movements. In this work, we use a sensor-based motion tracking system to capture 3D hand and finger motions. To detect and recognize hand gestures, we propose a novel angular-velocity method, which is directly applied to real-time 3D motion data streamed by the sensor-based system. Our approach is capable of recognizing both static and dynamic gestures in real-time. We assess the recognition accuracy and execution performance with two interactive applications that require gesture input to interact with the virtual environment. Our experimental results show high recognition accuracy, high execution performance, and high-levels of usability.
机译：手势是人类环境相互作用的自然和直观形式，可以用作人机相互作用（HCI）中的输入替代，以增强可用性和自然。许多现有方法已经采用了视觉的基于视觉来检测和识别手势。然而，基于视觉的系统通常要求用户在限制空间内移动他们的手，其中光学装置可以捕获手的运动。而且，基于视觉的系统可能由于特勤手指运动而遭受自闭锁问题。在这项工作中，我们使用基于传感器的运动跟踪系统来捕获3D手和手指运动。为了检测和识别手势，我们提出了一种新颖的角速度方法，该方法直接应用于由基于传感器的系统流流的实时3D运动数据。我们的方法能够实时识别静态和动态手势。我们评估了具有两个交互式应用程序的识别准确性和执行性能，这些应用程序需要手势输入来与虚拟环境进行交互。我们的实验结果显示了高识别精度，高执行性能和高级别的可用性。

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