一种基于Leap Motion的直观体交互技术

Leap Motion系统可高精度实时检测并跟踪手掌、手指的位置和速度等信息,为大屏幕、远距离体交互提供有效途径。通过观察用户手势远距离操控现实环境中的物体,该文设计易于理解的体交互操作手势;借助对Leap Motion传感器工作区域的远近场划分,建立合理的物理空间和信息空间映射关系;针对利用深度相机的手部姿态解算问题,结合Leap Motion传感器的特点,提出一种结合手掌法向和指尖向量的空间姿态算法;结合大屏幕体交互的任务特点,设计了支持连续数据流的非接触式体交互原型系统;最后通过用户实验对原型系统进行评估。结果表明,和传统的2D体交互比较,该文算法的用户体验和表现更为自然、直观和高效。     

基于深度信息的指尖追踪及手势识别

提出了一个基于深度信息对手指和手部进行实时跟踪,并可用于手势识别的方案。用Kinect获取深度信息,然后生成手部的三维点云,进行过滤转换成像素矩阵;使用K-curvature算法获取指尖和手掌方位,然后通过手指之间的相关距离进行手指标定。实验结果证明该方案识别追踪效果稳定且高效,不受光照和复杂背景影响,能够同时跟踪双手共10个手指和2个掌心的动作轨迹,并用于手势识别。     

基于嵌入式无线局域网接入控制器协议模型研究

为解决无线通信日趋多功能化的问题,以无线局域网接八控制器系统为对象,采用嵌入式Lmux的系统硬件结构和软件解决方案,设计了蓝牙协议在嵌入式Linux系统中实现模型,并实现了关键技术TCP/IP与蓝牙协议转换和HCl接口．系统在试验测试环境中能够完成无线设备的接八功能,运行稳定,上层协议与L2CAP层传送数据包长度由原来的127byte增至1514byte,通信效率得到了改善．     

使用肤色模型和动态跟踪窗口的手势跟踪算法

手势分析是HCI（Human Computer Interaction）技术的重要研究领域,而手势跟踪是手势分析的的一项重要步骤。提出了一种结合肤色模型和动态跟踪窗口的手势跟踪算法。算法首先在序列图像中进行肤色检测将图像二值化,然后用动态跟踪窗口逼近手部区域。实验结果表明,该算法是有效的,同时也能满足实时性的要求。     

一种基于Modbus协议的嵌入式工控系统人机交互的设计

随着嵌入式系统的发展及其广泛应用,人机交互的需要越来越大.本文在介绍了Modbus通信协议和人机交互组成结构的基础上,设计了基于Modbus通信协议的嵌入式微处理器系统与MT500系列触摸屏通信的实现方案.该方案通信稳定可靠,程序可移植性强,取得了理想的效果.     

一种基于设计模式的GIS HCI复用框架

基于特征数据模型的GIS软件开发是一个重要方向。这就需要建立符合其特征的软件平台,以提高开发效率,减少重复应用。该文从软件框架的角度,利用设计模式分析了特征模型的软件结构,提出了GIS HCI框架,并进行了分析。     

基于认知心理学知识的人机界面设计

提出把认知心理学知识应用于人机界面设计的理念。首先对传统人机界面设计方法的缺陷进行了分析,然后论述如何利用认知心理学知识建立合理、有效、良好的人机界面,并总结出设计人机界面的多个原则。运用这些原则进行实际设计取得了良好的效果。     

Ferret: Reviewing Tabular Datasets for Manipulation

How do we ensure the veracity of science? The act of manipulating or fabricating scientific data has led to many high-profile fraud cases and retractions. Detecting manipulated data, however, is a challenging and time-consuming endeavor. Automated detection methods are limited due to the diversity of data types and manipulation techniques. Furthermore, patterns automatically flagged as suspicious can have reasonable explanations. Instead, we propose a nuanced approach where experts analyze tabular datasets, e.g., as part of the peer-review process, using a guided, interactive visualization approach. In this paper, we present an analysis of how manipulated datasets are created and the artifacts these techniques generate. Based on these findings, we propose a suite of visualization methods to surface potential irregularities. We have implemented these methods in Ferret, a visualization tool for data forensics work. Ferret makes potential data issues salient and provides guidance on spotting signs of tampering and differentiating them from truthful data.     

面向虚实融合的人机交互

面向虚实融合的人机交互涉及计算机科学、认知心理学、人机工程学、多媒体技术和虚拟现实等领域，旨在提高人机交互的效率，同时响应人类认知与情感的需求，在办公教育、机器人和虚拟/增强现实设备中都有广泛应用。本文从人机交互涉及感知计算、人与机器人交互及协同、个性化人机对话和数据可视化等4个维度系统阐述面向虚实融合人机交互的发展现状。对国内外研究现状进行对比，展望未来的发展趋势。本文认为兼具可迁移与个性化的感知计算、具备用户行为深度理解的人机协同、用户自适应的对话系统等是本领域的重要研究方向。     

A Comprehensive Review of Data-Driven Co-Speech Gesture Generation

Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology for creating believable characters in film, games, and virtual social spaces, as well as for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. The field of gesture generation has seen surging interest in the last few years, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text and non-linguistic input. Concurrent with the exposition of deep learning approaches, we chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method (e.g., optical motion capture or pose estimation from video). Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.