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人与计算机互动的探索:在具有挑战性的环境中进行手势识别管理

Victor Chang¹, Rahman Olamide Eniola², Lewis Golightly²

¹Aston University, Aston St, Birmingham, B4 7ET UK.

SN computer science

|June 19, 2023

概括

这项研究通过开发手势识别系统来增强语音障碍者的人与计算机的互动. 图像细分改进了卷积神经网络 (CNN) 模型.

科学领域:

计算机科学计算机科学
人与计算机的交互
人工智能的人工智能

背景情况:

语音障碍者经常依靠手势进行沟通,但在HCI研究中代表性不足.
现有的人机交互系统缺乏语音障碍者社区的可访问性.
开发直观的交互方法对于包容性至关重要.

研究的目的:

为语言障碍者社区创建一个可访问的手势识别系统.
为了提高人机交互的效率和无需外部设备的轻松性.
解决语音障碍者在HCI和自动化研究中的代表性不足问题.

主要方法:

一个双相算法,涉及兴趣区域 (ROI) 分段和卷积神经网络 (CNN) 图像分类.
颜色空间分割技术,以将手势与背景隔离.
使用Python Keras包进行CNN模型训练和图像分类.

主要成果:

开发的系统证明了图像细分对于有效的手势识别的必要性.
最优的CNN模型实现了58%的性能准确性.
与图像分割相比,性能增加了大约10%,而没有.

结论:

图像细分是提高手势识别准确性的关键组成部分.
开发的系统为语言障碍者提供了一条通往更具包容性的人机交互的途径.
进一步的研究可以建立在这些发现的基础上,以提高数字环境中的可访问性.

关键词:

卷积神经网络 (CNN) 是一种神经网络.手的识别手的识别.人与计算机的互动.机器学习是机器学习.

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