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颖的机器学习算法，以实现大型数据流的实时视频和传感器数据分析，但给定有限的计算资源。这项工作重点是医疗保健作为应用程序领域，实时视频分析可以防止操作医疗设备中的用户纠正或向看护人提供有关危险情况的警报。 该研究将开发算法以自动调整数据分析方法，尽管可用的计算资源有限，但在短时间内的分析精度最大化。当今的医疗环境比以往任何时候都更加精致。现在，许多医疗设备都经常在患者的家中使用，从甘蔗和轮椅等简单设备到精致的物品，例如葡萄糖仪，门诊输液泵和笔记本电脑大小的呼吸机。快速发展的家庭健康行业引起了人们对在家庭环境中不当使用设备使用的新安全问题。拟议的研究旨在通过开发进行实时视频分析的计算算法来减少与医疗设备相关的使用，并在不适当使用医疗设备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测老年人或残疾人活动的医疗保健系统也至关重要，以便让护理人员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，目的是从大量培训示例中学习，预测功能（i）在做出有效的预测方面非常准确，并且（ii）可以在指定的计算机系统上有效地运行以提供关键的时间结果。研究了三种类型的预测模型，以解决自动硬件适应的问题，包括基于向量的模型，基于矩阵的模型以及基于复制内核Hilbert Space（RKHS）函数的预测模型。 正在开发一般框架和多个优化技术，以学习与有限的内存和计算能力相匹配的准确预测模型。新的学习算法将在几种医疗情况下通过实时预测患者的活动来评估，从几个医疗保健相关项目收集的大型视频档案中的观察结果进行实时预测。 拟议工作的智力优点在于弥合预测模型的高复杂性与有限的计算资源之间的差距，这是一种在医疗保健以外许多应用领域中遇到的方案。机器学习算法和理论的拟议研究将使在给定计算基础架构的限制内对大数据进行复杂的预测算法。开发的用于自动硬件改编的技术将应用于一个大型的连续视频和传感器录音数据集，以进行医学关键的活动识别。 该项目的更广泛的影响包括为医学专家提供算法和工具，以支持新方法来分析观察数据以寻求认识和表征人类行为。具有连续观察的监视系统将能够通过共同置于的，有限的硬件将显着事件分类。来自连续流的复杂数据的研究人员将能够使用可用的计算资源在受约束的时间内以更高的精度探索其域。同样，可以使用有限的硬件来尽快利用大型档案。