Adaptive high degree-of-freedom interaction techniques

自适应高自由度交互技术

• 批准号: 298224-2007
• 负责人: Oore, Sageev
• 金额: $ 1.31万
• 依托单位: Saint Mary's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=366561
• 关键词: Adaptive; degree; freedom; interaction; techniques

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.

虽然计算能力和信息访问在过去几十年里确实增加了几个数量级，但人类的原始能力（例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保持着相似的事实。然而，常见的键盘/鼠标/显示器三重奏很难充分发挥人类运动和感知能力的潜力。这一点的意义在于，对于当今大量数据和计算的任务来说，这是事实。 涉及到，许多科学/创意工作流程中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，我对允许人们在极其复杂的水平上表演的乐器着迷：需要和奖励复杂的认知和运动技能并且可以流畅地用于实现复杂想法的乐器。我有兴趣发现这些强大的交互技术的构建模块，以及允许某些人进行某些操作的品质。 作为一名计算机科学研究人员，我希望创建能够在数字领域内提供这种流畅交互的乐器：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的工具。这些观察引出了推动我的长期研究计划的一个关键问题：我们如何在高自由度（DOF）音频/视觉/触觉空间中设计交互，使用户能够 查看复杂数据并与之有效交互？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，都有共同的原理、想法和技术来实现有效和流畅的高自由度交互，而发现和识别这些是我研究的核心。由于这些是我的目标，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针对大型复杂数据集的实用界面将是我的方法的强大而实用的元素。