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

本研究计划将开发一种理论和方法，用于移动多任务设置中工作负载的动态控制，包括移动、可穿戴和车辆与应用程序的交互。如何让应用程序了解当前的工作负载需求和任务上下文，以便它们能够动态地重新配置自己，以优化特定用户概要文件当前上下文中的安全性和生产力？这项研究将通过开发新型自适应界面来回答这个问题，通过对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来提供信息。相关用例包括驾驶员在驾驶过程中与车载技术交互，在矿山或建筑工地中使用移动应用程序导航障碍物，在通用航空中操作轻型飞机并执行多项任务的飞行员，或者使用手持或可穿戴设备行走，同时接收朋友和地图应用程序的信息和导航指示。******本研究将为移动、可穿戴和车辆环境中的自适应接口开发一个整体模型，其中自适应基于对主要任务关键参数的动态控制。与整体模型一起，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过载和注意力不集中的区域，这些区域表明需要在不同的任务环境中进行适应，开发特定于任务的适应触发指标，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应用程序的交互，仪表化飞机或飞行模拟器的日志数据）将用于不同环境下的动态用户分析和自适应界面的个性化。******这项工作是新颖的，因为保护主要任务免受移动、可穿戴或车载应用程序干扰影响的通用自适应接口尚不存在。虽然已经为驾驶任务开发了一些自适应界面，但它们的范围有限，而且还没有推广到一系列移动环境中。我打算开发一个强大的适应性界面的科学模型，它可以专门用于不同的移动使用环境。******这项工作对于设计用于移动环境中多任务使用的应用程序非常重要。这项研究的结果将支持尖端应用程序的开发，如安全驾驶员通知系统、基于智能手机使用导致的步态中断的老年用户跌倒风险评估、工业环境中使用的移动应用程序的安全性增强，以及改进通用航空飞行员的界面。

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.