Yasen Mais, Jusoh Shaidah
Department of Computer Science, Princess Sumaya University for Technology, Amman, Jordan.
PeerJ Comput Sci. 2019 Sep 16;5:e218. doi: 10.7717/peerj-cs.218. eCollection 2019.
With the development of today's technology, and as humans tend to naturally use hand gestures in their communication process to clarify their intentions, hand gesture recognition is considered to be an important part of Human Computer Interaction (HCI), which gives computers the ability of capturing and interpreting hand gestures, and executing commands afterwards. The aim of this study is to perform a systematic literature review for identifying the most prominent techniques, applications and challenges in hand gesture recognition.
To conduct this systematic review, we have screened 560 papers retrieved from IEEE Explore published from the year 2016 to 2018, in the searching process keywords such as "hand gesture recognition" and "hand gesture techniques" have been used. However, to focus the scope of the study 465 papers have been excluded. Only the most relevant hand gesture recognition works to the research questions, and the well-organized papers have been studied.
The results of this paper can be summarized as the following; the surface electromyography (sEMG) sensors with wearable hand gesture devices were the most acquisition tool used in the work studied, also Artificial Neural Network (ANN) was the most applied classifier, the most popular application was using hand gestures for sign language, the dominant environmental surrounding factor that affected the accuracy was the background color, and finally the problem of overfitting in the datasets was highly experienced.
The paper will discuss the gesture acquisition methods, the feature extraction process, the classification of hand gestures, the applications that were recently proposed, the challenges that face researchers in the hand gesture recognition process, and the future of hand gesture recognition. We shall also introduce the most recent research from the year 2016 to the year 2018 in the field of hand gesture recognition for the first time.
随着当今技术的发展，并且由于人类在交流过程中倾向于自然地使用手势来阐明意图，手势识别被认为是人机交互（HCI）的重要组成部分，它使计算机能够捕捉和解释手势，并随后执行命令。本研究的目的是进行系统的文献综述，以确定手势识别中最突出的技术、应用和挑战。
为了进行这项系统综述，我们筛选了从2016年到2018年发表在IEEE Xplore上检索到的560篇论文，在搜索过程中使用了“手势识别”和“手势技术”等关键词。然而，为了聚焦研究范围，排除了465篇论文。只研究了与研究问题最相关的手势识别作品以及组织良好的论文。
本文的结果可总结如下；在研究的工作中，带有可穿戴手势设备的表面肌电图（sEMG）传感器是最常用的采集工具，人工神经网络（ANN）是应用最多的分类器，最流行的应用是使用手势进行手语，影响准确性的主要环境因素是背景颜色，最后数据集中的过拟合问题非常普遍。
本文将讨论手势采集方法、特征提取过程、手势分类、最近提出的应用、手势识别过程中研究人员面临的挑战以及手势识别的未来。我们还将首次介绍2016年至2018年手势识别领域的最新研究。
