HCC-Small: A Cognitive Assistive System for Coaching the Use of Home Medical Devices
HCC-Small:用于指导家庭医疗设备使用的认知辅助系统
基本信息
- 批准号:0812465
- 负责人:
- 金额:$ 45万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2008
- 资助国家:美国
- 起止时间:2008-08-01 至 2012-07-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This research project will develop methods and algorithms to assist people with the procedures required to operate their home medical devices without errors. The goal is for the system to be trained on sample recordings of the person operating the device correctly, so that later it will be able to detect deviations from the correct operation sequence in the currently performed procedure, and automatically provide corrective feedback to the user, including segments of the video portions that show the appropriate steps. The project provides a new paradigm for learning by observation that does not require complete understanding of detailed activities in arbitrary visual and sensor sequences, but merely aligns a given new sequence in known context with previously established training data to detect significant deviations. The approach has four components: (1) defining the key states in an operational procedure and the sensors required to best detect and later communicate the proper operation of a portable home medical device; (2) training the system by observing multiple correct operations; (3) observing a new instance of the operation sequence and recognizing that this operation deviates from the training data in a significant way; and (4) providing corrective feedback to the user in the form of audio and video prompts. The research aims to understand the common types of steps required in the operations of home medical devices, map how the critical indicators of these steps can be detected through appropriate sensors, train a system to recognize these steps in the context of a specific human operator, establish a range of required training repetitions for different operational step types and corresponding sensors, and provide a set of suitable interventions to the end user when errors occur. The experiments will establish the range of training data set sizes for the automated classification of device operations. The research expects to yield a taxonomy of typical operational steps from an observational perspective for a set of devices such as infusion pumps, and establish the most effective sensors or sensor combinations to detect the successful completion of each type of step. In addition, the work will help find suitable passages in the video portion of the training observations to use as corrective feedback, together with other interactive dialog interventions that may be appropriate for the particular user. The long-term goal of this research is to develop a cognitive assistance system to learn and represent sequences of steps in the operation of home medical devices through multi-sensor observation and interaction with a human operator. Examples of some of the targeted home healthcare devices are respirators and nebulizers (to help breathing), dialysis machines, infusion pumps, home monitoring devices for blood pulse oxygen, EEG, and ECG. The project will develop a means for home users of these devices to ensure that the correct procedures are followed and accurate operations result. The system provides ongoing feedback to assist users in their device operation by ?watching? the process via different sensing technologies and providing appropriate guidance when required. The target population immediately benefiting from this work would be patients with mild cognitive impairments who would be supported with the automated coach in their use of home medical devices. The growing user base includes elderly people living at home, but requiring support from home medical devices. These medical devices can allow a patient to live independently with minimal assistance, as long as the home medical devices provide the required health support. The end result may be a reduction in errors and in the number of calls for assistance in the operation and maintenance of home medical devices. This will allow people to live independently at home for an average longer period than at present and thereby reduce health care system costs. The research is valuable for medical device companies with respect to device design, verification, and validation processes, offering insights into what sensors and communication devices could be most beneficial for integration into the device itself.
该研究项目将开发方法和算法,以帮助人们正确操作家用医疗设备。 目标是对系统进行正确操作设备的人员的样本记录的训练,以便稍后能够检测当前执行的程序中与正确操作顺序的偏差,并自动向用户提供纠正反馈,包括显示适当步骤的视频部分的片段。 该项目提供了一种通过观察进行学习的新范式,不需要完全理解任意视觉和传感器序列中的详细活动,而只是将已知背景下的给定新序列与先前建立的训练数据进行比对,以检测重大偏差。该方法有四个组成部分:(1)定义操作过程中的关键状态和传感器,以最好地检测便携式家用医疗设备的正确操作并随后进行通信;(2)通过观察多个正确操作来训练系统;(3)观察操作序列的新实例并识别该操作以显著的方式偏离训练数据;以及(4)以音频和视频提示的形式向用户提供校正反馈。该研究旨在了解家用医疗设备操作中所需的常见步骤类型,绘制如何通过适当的传感器检测这些步骤的关键指标,训练系统在特定人类操作员的背景下识别这些步骤,为不同的操作步骤类型和相应的传感器建立所需的训练重复范围,并在发生错误时向终端用户提供一组适当的干预。 实验将建立用于设备操作自动分类的训练数据集大小的范围。 该研究预计将从观察的角度对输液泵等一组设备的典型操作步骤进行分类,并建立最有效的传感器或传感器组合来检测每种类型步骤的成功完成。此外,这项工作将有助于在训练观察的视频部分中找到合适的段落,以用作纠正反馈,以及其他可能适合特定用户的交互式对话干预。 本研究的长期目标是开发一种认知辅助系统,通过多传感器观察和与人类操作员的交互来学习和表示家用医疗设备操作中的步骤序列。一些目标家庭医疗保健设备的示例是呼吸机和雾化器(以帮助呼吸)、透析机、输液泵、用于血液脉搏氧、EEG和ECG的家庭监测设备。该项目将为这些设备的家庭用户开发一种方法,以确保遵循正确的程序和准确的操作结果。该系统提供持续的反馈,以协助用户在他们的设备操作?看?通过不同的传感技术,并在需要时提供适当的指导。直接受益于这项工作的目标人群将是轻度认知障碍的患者,他们将在使用家用医疗设备时得到自动教练的支持。不断增长的用户群包括住在家里但需要家庭医疗设备支持的老年人。这些医疗设备可以允许患者在最小的帮助下独立生活,只要家庭医疗设备提供所需的健康支持。最终结果可能是减少错误和家庭医疗设备的操作和维护中的求助次数。这将使人们在家中独立生活的平均时间比目前更长,从而降低医疗保健系统的成本。该研究对于医疗器械公司在器械设计、验证和确认过程方面具有重要价值,可以深入了解哪些传感器和通信设备最有利于集成到器械本身中。
项目成果
期刊论文数量(0)
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Alexander Hauptmann其他文献
Learning to Identify TV News Monologues by Style and Context
学习根据风格和背景识别电视新闻独白
- DOI:
- 发表时间:
2003 - 期刊:
- 影响因子:0
- 作者:
Cees G. M. Snoek;Alexander Hauptmann - 通讯作者:
Alexander Hauptmann
Towards a Large Scale Concept Ontology for Broadcast Video
广播视频的大规模概念本体
- DOI:
10.1007/978-3-540-27814-6_78 - 发表时间:
2004 - 期刊:
- 影响因子:0
- 作者:
Alexander Hauptmann - 通讯作者:
Alexander Hauptmann
News-on-Demand: An Application of Informedia® Technology
新闻点播:Infomedia® 技术的应用
- DOI:
10.1045/september95-hauptmann - 发表时间:
1995 - 期刊:
- 影响因子:0
- 作者:
Alexander Hauptmann;M. Witbrock;Michael G. Christel - 通讯作者:
Michael G. Christel
Distinction of stress and non-stress tasks using facial action units
使用面部动作单元区分压力和非压力任务
- DOI:
- 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
Carla Viegas;S. Lau;R. Maxion;Alexander Hauptmann - 通讯作者:
Alexander Hauptmann
Vox Populi Annotation: Measuring Intensity of Ideological Perspectives by Aggregating Group Judgments
Vox Populi解读:通过聚合群体判断来衡量意识形态观点的强度
- DOI:
- 发表时间:
2008 - 期刊:
- 影响因子:0
- 作者:
Wei;Alexander Hauptmann - 通讯作者:
Alexander Hauptmann
Alexander Hauptmann的其他文献
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{{ truncateString('Alexander Hauptmann', 18)}}的其他基金
Student Travel Support for 2019 ACM International Conference on Multimedia (ACM MM)
2019 年 ACM 国际多媒体会议 (ACM MM) 学生旅行支持
- 批准号:
1937998 - 财政年份:2019
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
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 - 财政年份:2016
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
EAGER: Controlling a Robotic Third Hand - Exploring Use of Distributed Intelligence from Autonomy to Brain Machine Interfaces for Augmenting Human Capability
EAGER:控制机器人第三只手 - 探索使用从自主到脑机接口的分布式智能来增强人类能力
- 批准号:
1650994 - 财政年份:2016
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
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:
1251187 - 财政年份:2013
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
DC: Small: Semantic Analysis of Large Multimedia Data Sets
DC:小型:大型多媒体数据集的语义分析
- 批准号:
0917072 - 财政年份:2009
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
CRI: CRD: Collaborative Research: Large Analytics Library and Scalable Concept Ontology for Multimedia Research
CRI:CRD:协作研究:用于多媒体研究的大型分析库和可扩展概念本体
- 批准号:
0751185 - 财政年份:2008
- 资助金额:
$ 45万 - 项目类别:
Standard Grant
A Video Indexing Ontology Using Fuzzy Metadata
使用模糊元数据的视频索引本体
- 批准号:
0535056 - 财政年份:2005
- 资助金额:
$ 45万 - 项目类别:
Continuing Grant
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