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基于系统的感手套用于手语识别的研究进展：2007 年至 2017 年的综述

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017.

机构信息

Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak 35900, Malaysia.

Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit 34001, Iraq.

出版信息

Sensors (Basel). 2018 Jul 9;18(7):2208. doi: 10.3390/s18072208.

DOI:10.3390/s18072208
PMID:29987266
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6069389/
Abstract

Loss of the ability to speak or hear exerts psychological and social impacts on the affected persons due to the lack of proper communication. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand. Innovative technology for this matter is mainly restricted and dispersed. The available trends and gaps should be explored in this research approach to provide valuable insights into technological environments. Thus, a review is conducted to create a coherent taxonomy to describe the latest research divided into four main categories: development, framework, other hand gesture recognition, and reviews and surveys. Then, we conduct analyses of the glove systems for SLR device characteristics, develop a roadmap for technology evolution, discuss its limitations, and provide valuable insights into technological environments. This will help researchers to understand the current options and gaps in this area, thus contributing to this line of research.

摘要

由于缺乏适当的交流，丧失说话或听力的能力会对患者造成心理和社会影响。已经实施了多种系统的学术干预措施，这些措施根据具体情况而有所不同，旨在克服与残疾相关的困难。基于感应手套的手语识别 (SLR) 系统是一项重要的创新，旨在获取有关人手形状或运动的数据。这方面的创新技术主要受到限制和分散。在这种研究方法中，应该探索可用的趋势和差距，以提供对技术环境的有价值的见解。因此，进行了一项综述，以创建一个连贯的分类法来描述最新的研究，分为四个主要类别：开发、框架、其他手势识别以及综述和调查。然后，我们分析了用于 SLR 设备的手套系统的特点，制定了技术演进路线图，讨论了其局限性，并深入了解了技术环境。这将帮助研究人员了解该领域的当前选择和差距，从而为该研究领域做出贡献。

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