HCC: Enabling Practical Human Activity Modeling for Interactive Applications

这是一个提供改进的项目，可以使传感器驱动的人类情况模型的新兴技术从实验室演示发展到在人机交互系统中实际广泛使用。我们今天看到的界面在很大程度上是静态的？无论它们在什么人类环境中使用，它们的行为方式都是一样的。在过去，信息工作者在固定的办公室环境中工作，这是可以接受的。然而，随着技术的进步，廉价的计算设备可以进入日常生活的所有不同环境，这将不再合适。我们的设备应该根据每种情况的人类环境进行调整，以最大限度地提高其易用性和有效性，以满足这些不同情况下的人类需求。然而，目前大多数交互系统没有关于它们所在的人类环境或它们所服务的用户的活动的信息。这是创建更适合于它们现在所处的更广泛世界的接口的重大障碍。为了克服这一基本障碍，一个新兴的工作机构已经开始开发对人类情况和活动进行建模的技术。这项工作的很大一部分使用了传感器驱动的统计模型，这些模型是用机器学习技术创建的。这些模型提供了对活动和情况的有用估计。然而，这些技术的广泛使用存在许多严重的实际障碍。这笔赠款将支持旨在克服这些障碍中最重要的障碍的积极工作。其中最重要的问题之一是最终用户收集足够的培训数据以使这些系统正常工作所需的费用、难度和破坏性。为解决这一问题，该项目包括开发和研究一系列创新技术，以减少与收集所需培训范例相关的人力成本。此外，还将开发互联网规模的培训数据收集新技术。这些技术将使从目前从少数人那里收集大量数据的做法转变为从许多人那里收集少量数据。作为对这一方法的补充，将开发新的分层建模技术，以便从反映许多人的平均行为的初始通用模型平稳而快速地过渡到在对该最终用户的干扰最小的情况下根据个人的具体情况进行精细调整的模型。最后，这里将资助的工作将探索机器学习技术中尚未广泛应用于人机交互应用的各种新兴进步的有效性。这项研究的影响将远远超出它所攻击的特定问题，因为它本质上是使能的？寻求开辟一种可能性，即应该是一类完整的新接口技术。此外，这项工作对于支持特殊需求人群的一些应用程序尤其重要，该项目包括加强教育的活动。

This is a project to provide improvements which may enable the emerging technology of sensor-driven models of human situation to move from laboratory demonstrations to practical widespread use in human-computer interaction systems. Interfaces as we see them today are largely static ? they act in the same way regardless of what human situations they are used in. In the past world of information workers in a fixed office setting, this was acceptable. However, as technological advances allow inexpensive computing devices to move into all the diverse settings of everyday life, this will no longer be appropriate. Our devices should adjust to the human context of each situation in a way that maximizes their ease of use and effectiveness in serving human needs across those varying situations. However, currently most interactive systems have no information about the human situations they are operating in, or the activities of the users they serve. This represents a significant barrier to creating interfaces which are more appropriate to the wider world they are now being placed in.To overcome this basic obstacle, an emerging body of work has begun to develop techniques for modeling human situations and activities. Much of this work employs sensor-driven statistical models created with machine learning techniques. These models provide useful estimates of activities and situations. However, there are a number of serious practical barriers to widespread use of these techniques. This grant will support an aggressive body of work aimed at overcoming the most important of these barriers. One of the most important of them is the expense, difficulty, and disruptiveness to end-users of collecting sufficient training data to make these systems work. To address this problem, the project includes development and study of a collection of innovative techniques tuned to reducing the human costs associated with collecting the required training examples.In addition, new techniques for internet-scale collection of training data will be developed. These techniques will enable a shift from the current practice of collecting a large amount of data from a few people to collection of a small amount of data from many people. Complementing this approach, new hierarchical modeling techniques will be developed which should allow a smooth and rapid transition from an initial generic model reflecting average behavior of many people, to models which are finely tuned to the particulars of an individual with minimal disruption for that end-user. Finally, the work to be funded here will explore the effectiveness of a variety of emerging advances in machine learning technology which have yet to be widely applied to human-computer interaction applications.The impact of this research will go significantly beyond the specific issues it attacks because it is enabling in nature ? seeking to open up the possibility of what should be complete classes of new interface technology. In addition, this work is particularly important for some applications supporting special needs populations, and the project includes activities to enhance education.