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Assistive pointer device for limb impaired people: A novel Frontier Point Method for hand movement recognition

机译:肢体残障人士辅助指示器设备:一种用于手部动作识别的新颖前沿点方法

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摘要

In this modern era, the use of computer technology and computing devices play significant role in every day human activities. From the disabled people perspective, there is huge demand to improve Human-Computer Interaction (HCI), to overcome their difficulty in using the standard interactive devices. Basically, HCI provides a way for humans to interact with a computer using a keyboard, a mouse, and other input devices in real-time. This paper proposes a novel assistive pointer device called Frontier Point method (FPM), which is based on a hand movement recognition technique. The proposed hand movement recognition technique primarily focuses on the direction of hand movement for dynamic recognition in real-time using least square fitting and virtual frame techniques. Next based on boundary values, such that if the hand crosses a boundary value of a given quadrant, then a SENDKEY stroke is generated that corresponds to that range. This method is implemented with the help of a depth sensor camera called Kinect. Kinect takes the RGB data and depth data of the human skeleton and generates coordinate information corresponding to specific body joints. Experiments were conducted in which different users were evaluated for their ability to navigate a PowerPoint presentation multiple times. Collectively, an average recognition time of 2.386 s was calculated with an average recognition rate of 97.37%.
机译:在这个现代时代,计算机技术和计算设备的使用在人类日常活动中发挥着重要作用。从残疾人的角度来看,有巨大的需求来改善人机交互(HCI),以克服他们使用标准交互设备的困难。基本上,HCI为人类提供了一种使用键盘,鼠标和其他输入设备与计算机进行实时交互的方式。本文提出了一种新颖的辅助指针设备,称为Frontier Point method(FPM),它是基于手部运动识别技术的。提出的手部动作识别技术主要集中在手部移动的方向上,以使用最小二乘拟合和虚拟框架技术进行实时动态识别。接下来基于边界值,例如,如果手越过给定象限的边界值,则将生成与该范围相对应的SENDKEY笔划。该方法是在称为Kinect的深度传感器摄像机的帮助下实现的。 Kinect获取人体骨骼的RGB数据和深度数据,并生成与特定身体关节相对应的坐标信息。进行了一些实验,其中评估了不同用户多次浏览PowerPoint演示文稿的能力。总的来说,平均识别时间为2.386 s,平均识别率为97.37%。

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