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处境模型的新兴技术从实验室演示转移到人机交互系统的实际广泛应用。我们今天看到的接口大部分是静态的？不管它们被用于什么样的人类情境，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，廉价的计算设备进入日常生活的各种设置，这将不再合适。我们的设备应该适应每种情况下的人类背景，以最大限度地提高其易用性和有效性，以满足不同情况下人类的需求。然而，目前大多数交互系统都没有关于它们所操作的人类环境或它们所服务的用户活动的信息。这对于创建更适合于现在所处的更广阔世界的接口来说是一个重大障碍。为了克服这一基本障碍，一项新兴的工作已经开始开发人类情境和活动建模的技术。这项工作大部分采用了由机器学习技术创建的传感器驱动的统计模型。这些模型提供了对活动和情况的有用估计。然而，这些技术的广泛应用存在一些严重的实际障碍。这笔赠款将支持一系列旨在克服这些最重要障碍的积极工作。其中最重要的一个问题是，收集足够的训练数据以使这些系统工作的成本、难度和对最终用户的破坏性。为了解决这个问题，该项目包括开发和研究一组创新技术，以减少与收集所需训练示例相关的人力成本。此外，将开发用于互联网规模的训练数据收集的新技术。这些技术将使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的做法。作为对这一方法的补充，将开发新的分层建模技术，它将允许从反映许多人的平均行为的初始通用模型平稳和快速地过渡到对最终用户的个体细节进行精细调整的模型。最后，这里资助的工作将探索机器学习技术的各种新兴进展的有效性，这些技术尚未广泛应用于人机交互应用。这项研究的影响将大大超出它所攻击的具体问题，因为它在本质上是可行的？寻求打开什么应该是完整类的新接口技术的可能性。此外，这项工作对一些支助特殊需要人口的应用特别重要，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.