Adaptive high degree-of-freedom interaction techniques

在过去的几十年里，计算能力和信息获取能力确实以数量级的速度增长，但人类的原始能力，如我们的感知和运动系统--尽管令人惊讶--从一代到下一代仍然基本相似。普通键盘/鼠标/显示器三重奏很难发挥人类运动和感知能力的全部潜力。这一点的意义在于，对于今天的大量数据和由于涉及到计算，许多科学/创意工作流程的关键瓶颈现在是界面本身-人与机器之间的带宽。作为一名拥有丰富专业音乐经验的科学家，我对能够让人们以极其复杂的水平演奏的乐器着迷：需要和奖励复杂的认知和运动技能的工具，可以流畅地用于实现复杂的想法。我有兴趣发现这些强大的互动技术的构建块，作为一名计算机科学研究人员，我想创造出在数字领域提供这种流畅互动的乐器：这些工具可以让人类自然地驱动当今数字引擎的巨大处理能力，以实现他们的目标，并更直接地发挥他们的愿景。我的长期研究计划是：我们如何在高自由度（DOF）的音频/视觉/触觉空间中设计交互，使用户能够有效地查看复杂数据并与之交互？我探索了两种不同的数据交互测试平台：创建多媒体内容和可视化科学（航空航天和地震）数据。在这两种情况下，都有一些共同的原则、想法和技术来实现有效和流畅的高自由度交互，发现和识别这些是我研究的核心。因为这些是我的目标，机器学习算法将是实现这一目标的关键手段。使用概率自适应方法为用户提供一个在大型复杂数据集上的易用界面将是我方法中一个强大而实用的元素。

While it is true that computing power and information access has increased by orders of magnitude over the last few decades, it is also true that raw human abilities such as our perceptual and motor systems- astonishing as they are- remain effectively similar from one generation to the next.Yet, the common keyboard/ mouse/ monitor trio is hardly playing to the full potential of human motor and perceptual abilities.The significance of this is that for tasks where today's massive amounts of data and computation are involved,the critical bottleneck in many scientific/creative workflows is now the interface itself-- the bandwidth between the human and machine.As a scientist with extensive professional music experience,I am fascinated by instruments that allow people to perform at an extremely sophisticated level:instruments that require and reward complex cognitive& motor skills and that can be fluidly used to realize sophisticated ideas.I am interested in discovering building blocks of these powerful interaction techniques,and qualities that allow some instruments to be playable in this way.As a computer science researcher I want to create instruments that provide such fluid interaction within the digital realm: tools that let humans naturally drive the immense processing power of today's digital engines to reach their goals and play with their visions more directly.These observations lead to a key question driving my long-term research programme:How do we design interactions in high degree-of-freedom(DOF) audio/ visual/ haptic spaces that allow a user to view and interact effectively with complex data? I explore two different testbeds for data interaction:creating multimedia content and visualizing scientific (aerospace&seismic) data. In both cases,there are shared principles,ideas and techniques for effective and fluid high-DOF interaction,and discovering and identifying these is at the heart of my research.Inasmuch as these are my goals, machine learning algorithms will be a key means of achieving this.Using probabilistic adaptive methods to give the user a wieldy interface over the large complex data sets will be a powerful and practical element of my approach.