BIGDATA: Small: DA: Collaborative Research: Real Time Observation Analysis for Healthcare Applications via Automatic Adaptation to Hardware Limitations

BIGDATA：小型：DA：协作研究：通过自动适应硬件限制对医疗保健应用进行实时观察分析

• 批准号: 1638429
• 负责人: Alexander Hauptmann
• 金额: $ 9.78万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1638429&HistoricalAwards=false
• 关键词: BIGDATA; Small; DA; Collaborative; Research

This research seeks to develop novel machine learning algorithms that enable real-time video and sensor data analysis on large data streams given limited computational resources. The work focuses on healthcare as an application domain where real-time video analysis can prevent user-errors in operating medical devices or provide immediate alerts to caregivers about dangerous situations. The research will develop algorithms to automatically adapt data analysis approaches to maximize accuracy of analysis within a short time period despite limited available computing resources. Today's healthcare environment is significantly more technologically sophisticated than ever before. Many medical devices are now frequently used in patient's homes, ranging from simple equipment such as canes and wheelchairs to sophisticated items such as glucose meters, ambulatory infusion pumps and laptop-sized ventilators. The rapidly growing home health industry raises new safety concerns about devices being used inappropriately in the home setting. The proposed research is designed to reduce medical device related use-errors by developing computational algorithms that perform real-time video analysis and alert the patient or caregiver when medical devices are not used appropriately. The real-time video and sensor data analysis is also critical to the healthcare systems that monitor the activities of the elderly or those with disabilities in order to allow a caregiver to react immediately to an incident. New machine learning theories and algorithms will automatically adapt to hardware limitations, with the aim to learn from a large number of training examples, a prediction function that (i) is sufficiently accurate in making effective predictions and (ii) can be run efficiently on a specified computer system to deliver time critical results. Three types of prediction models are studied to address the problem of automatic hardware adaptation, including a vector-based model, a matrix-based model, and a prediction model based on a function from a Reproducing Kernel Hilbert Space (RKHS). A general framework and multiple optimization techniques are being developed to learn accurate prediction models that match limited memory and computational capacity. The new learning algorithms will be evaluated in several medical scenarios through real-time prediction of a patient's activities from observations in the large video archives collected by several healthcare related projects. The intellectual merit of the proposed work is in bridging the gap between the high complexity of a prediction model and limited computational resources, a scenario that is encountered in many application domains besides healthcare. The proposed research in machine learning algorithms and theories will make it possible to run complicated prediction algorithms on big data within the limitation of a given computing infrastructure. The developed techniques for automatic hardware adaptation will be applied to a large dataset of continuous video and sensor recordings for medically-critical activity recognition. The project's broader impacts include providing medical experts with algorithms and tools supporting novel approaches to analyzing observational data in their quest to recognize and characterize human behavior. Surveillance systems with continuous observations will be able to categorize salient events with co-located, limited hardware. Researchers with complex data from continuous streams will be able to explore their domains with greater accuracy within constrained time using their available computing resources. Similarly, large archives can be exploited as rapidly as possible with limited hardware.

这项研究旨在开发新的机器学习算法，在计算资源有限的情况下，能够对大数据流进行实时视频和传感器数据分析。这项工作的重点是医疗保健作为一个应用领域，实时视频分析可以防止用户在操作医疗设备时出现错误，或向护理人员提供有关危险情况的即时警报。 该研究将开发算法来自动适应数据分析方法，以在短时间内最大限度地提高分析的准确性，尽管可用的计算资源有限。今天的医疗保健环境比以往任何时候都更加先进。许多医疗设备现在经常在病人家中使用，从简单的设备如手杖和轮椅到复杂的物品如血糖仪、流动输液泵和笔记本电脑大小的冰箱。快速增长的家庭健康行业提出了新的安全问题，即在家庭环境中不适当地使用设备。拟议研究旨在通过开发计算算法来减少医疗器械相关的使用错误，该算法可执行实时视频分析，并在医疗器械使用不当时提醒患者或护理人员。实时视频和传感器数据分析对于监控老年人或残疾人活动的医疗保健系统也至关重要，以便护理人员能够立即对事件做出反应。新的机器学习理论和算法将自动适应硬件限制，目的是从大量的训练示例中学习预测函数，（i）在进行有效预测时足够准确，（ii）可以在指定的计算机系统上有效运行，以提供时间关键的结果。三种类型的预测模型进行了研究，以解决自动硬件自适应的问题，包括基于矢量的模型，基于矩阵的模型，和预测模型的基础上，从再生核希尔伯特空间（RKHS）的功能。 正在开发一个通用框架和多种优化技术，以学习匹配有限内存和计算能力的准确预测模型。新的学习算法将在几个医疗场景中进行评估，通过从几个医疗保健相关项目收集的大型视频档案中观察到的患者活动进行实时预测。 所提出的工作的智力价值是在弥合预测模型的高复杂性和有限的计算资源之间的差距，在许多应用领域，除了医疗保健遇到的情况。机器学习算法和理论的研究将使在给定计算基础设施的限制下对大数据运行复杂的预测算法成为可能。所开发的自动硬件自适应技术将应用于连续视频和传感器记录的大型数据集，用于医学关键活动识别。 该项目更广泛的影响包括为医学专家提供算法和工具，支持新的方法来分析观察数据，以识别和描述人类行为。具有连续观测功能的监视系统将能够利用同一地点的有限硬件对突出事件进行分类。拥有来自连续流的复杂数据的研究人员将能够利用其可用的计算资源在有限的时间内以更高的精度探索其领域。同样，可以利用有限的硬件尽可能快地利用大型档案。