首页> 外文期刊>ACM Transactions on Interactive Intelligent Systems >Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emojis in Computer-Mediated Communication
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Developing a Hand Gesture Recognition System for Mapping Symbolic Hand Gestures to Analogous Emojis in Computer-Mediated Communication

机译:开发一种手势识别系统,用于在计算机介导的通信中将符号手势映射到类似的表情符号

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摘要

Recent trends in computer-mediated communication (CMC) have not only led to expanded instant messaging through the use of images and videos but have also expanded traditional text messaging with richer content in the form of visual communication markers (VCMs) such as emoticons, emojis, and stickers. VCMs could prevent a potential loss of subtle emotional conversation in CMC, which is delivered by nonverbal cues that convey affective and emotional information. However, as the number of VCMs grows in the selection set, the problem of VCM entry needs to be addressed. Furthermore, conventional means of accessing VCMs continue to rely on input entry methods that are not directly and intimately tied to expressive nonverbal cues. In this work, we aim to address this issue by facilitating the use of an alternative form of VCM entry: hand gestures. To that end, we propose a user-defined hand gesture set that is highly representative of a number of VCMs and a two-stage hand gesture recognition system (trajectory-based, shape-based) that can identify these user-defined hand gestures with an accuracy of 82%. By developing such a system, we aim to allow people using low-bandwidth forms of CMCs to still enjoy their convenient and discreet properties while also allowing them to experience more of the intimacy and expressiveness of higher-bandwidth online communication.
机译:计算机介导通信(CMC)的最新趋势不仅导致通过使用图像和视频来扩展即时消息传递,而且还以表情符号,表情符号等视觉通信标记(VCM)的形式扩展了具有更丰富内容的传统文本消息传递和贴纸。 VCM可以防止在CMC中潜在的微妙的情感对话损失,这种对话是通过传达情感和情感信息的非语言线索传递的。但是,随着选择集中VCM数量的增加,需要解决VCM输入的问题。此外,访问VCM的常规方法继续依赖于不直接与表达性非言语提示直接联系的输入输入方法。在这项工作中,我们旨在通过促进使用VCM输入的另一种形式:手势来解决此问题。为此,我们提出了可高度代表许多VCM的用户定义手势集和两阶段手势识别系统(基于轨迹,基于形状),该系统可通过以下方式识别这些用户定义手势:准确性为82%。通过开发这样的系统,我们旨在允许使用低带宽形式的CMC的人们仍然享受其便捷而谨慎的特性,同时还使他们能够体验到更高带宽的在线交流的更多亲密感和表现力。

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