Dynamic Control of Task Demands in Mobile Contexts using Sensor Data and Adaptive User Models

该研究计划将开发一种理论和方法，用于动态控制移动的多任务环境中的工作负载，包括移动的，可穿戴和车辆与应用程序的交互。如何使应用程序了解当前的工作负载需求和任务上下文，以便它们可以动态地重新配置自己，从而在当前上下文中针对特定用户配置文件优化安全性和生产力？ 这项研究将通过开发新型自适应界面来回答这个问题，这些界面由传感器数据的模式分析和使用统计和机器学习技术开发的用户模型提供信息。相关用例包括驾驶员在驾驶时与车载技术进行交互，有人在矿井或建筑工地中导航障碍物时使用移动的应用程序，驾驶轻型飞机并执行通用航空中的多项任务的飞行员，或者使用手持或可穿戴设备并步行的人，同时接收来自朋友和地图应用程序的消息和导航指令。本研究将开发一个整体模型的自适应接口在移动的，可穿戴和车辆的情况下，适应是基于动态控制的主要任务的关键参数。沿着整体模型，我们将在选定的用例中为自适应接口开发专门的模型。该研究将确定任务参数空间中的过载和注意力不集中区域，这些区域表示需要在不同的任务环境中进行适应，开发适应触发器的特定任务指标，如困倦，分心或一般过载。** 相关数据存储库中的挖掘和机器学习（例如，仪表化车辆中的驾驶员交互、用户与包括内置步态分析的移动的应用程序的交互、来自仪表化飞机或飞行模拟器的日志数据）将用于动态用户简档和不同环境中的自适应界面的个性化。** 这项工作是新颖的，因为保护主要任务不受移动的、可穿戴或车载应用干扰影响的通用自适应界面尚不存在。虽然已经开发了一些自适应接口用于驾驶任务，但是它们在范围上是有限的，并且还没有推广到移动的上下文的范围。我打算开发一个强大的自适应界面的科学模型，可以专门用于不同的移动的使用环境。** 这项工作对于设计用于移动的环境中的多任务使用的应用程序具有重要意义。这项研究的结果将支持尖端应用程序的开发，例如安全驾驶员通知系统，基于智能手机使用造成的步态中断的老年用户福尔斯风险评估，工业环境中使用的移动的应用程序的安全增强，以及通用航空飞行员的改进界面。

This research program will develop a theory and method for dynamic control of workload in mobile multi-task settings, encompassing mobile, wearable, and vehicular interactions with applications. How can applications be made aware of current workload demands and task-context so that they can reconfigure themselves dynamically to optimize safety and productivity in the current context for a specific user profile? This research will answer this question by developing new types of adaptive interface, informed by pattern analysis of sensor data and user models developed using statistical and machine learning techniques. Relevant use cases include a driver interacting with in-vehicle technology while driving, someone using a mobile application while navigating obstacles in a mine or a construction site, a pilot operating a light aircraft and carrying out multiple tasks in general aviation, or a person using a hand-held or wearable device and walking, while receiving messages and navigation instructions from friends and a map application.******This research will develop an overall model for adaptive interfaces in mobile, wearable, and vehicular contexts where adaptation is based on dynamic control of key parameters of the primary task. Along with the overall model, we will develop specialized models for adaptive interfaces in selected use cases. The research will identify regions of overload and inattention in task parameter spaces that signal a need for adaptation in different task contexts, developing task-specific indicators of adaptation triggers such as drowsiness, distraction, or general overload. ******Mining and machine learning within relevant data repositories (e.g., driver interactions in instrumented vehicles, user interactions with mobile applications that include built-in gait analysis, log data from instrumented aircraft or flight simulators) will be used for dynamic user profiling and personalization of the adaptive interface in different contexts. ******This work is novel because general adaptive interfaces that protect the primary task from the interfering effect of a mobile, wearable, or in-vehicle application do not yet exist. While some adaptive interfaces have been developed for driving tasks, they are limited in scope and have not been generalized to a range of mobile contexts. I intend to develop a strong scientific model of adaptive interfaces that can be specialized for different mobile use contexts. ******This work will be highly significant for the design of applications intended for multi-task use in mobile settings. The results of this research will support the development of cutting edge applications such as safe driver notification systems, falls risk assessment for elderly users based on disruptions to gait caused by smartphone use, safety enhancement of mobile applications used in industrial settings, and improved interfaces for general aviation pilots.