SHB: Type I (EXP): Context-aware Ubiquitous Human Health Monitoring

SHB：I 类（EXP）：情境感知、无处不在的人体健康监测

• 批准号: 1231671
• 负责人: Weihua Sheng
• 金额: $ 35万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1231671&HistoricalAwards=false
• 关键词: SHB; Type; EXP; Context; aware

This project develops a ubiquitous human health monitoring system that collects not only vital signs, but also daily activities and environmental context of a human subject in an everyday life setting. From these collected data, higher level knowledge, such as behavioral and vital sign anomalies, is extracted to assist health evaluation, medical diagnosis/prognosis or healthcare delivery. The research team first develops a low-cost and power-aware wearable Smart Health Monitoring (SmartMon) system, which is integrated into human clothing with minimized obtrusiveness to the wearer. The research team then employs a minimalist approach to understand human?s daily activities and environmental context using the data collected by the SmartMon system. The research team further develops adaptive and intelligent signal processing algorithms to detect anomalies in human daily activities and vital signs for health assessment applications. This research can significantly improve the ubiquitousness, accuracy, and reliability of wearable human health monitoring. The technologies developed in this project have many different applications, from improving quality of life for elderly people to enhancing capability of first responders during emergency responses. Through the involvement of female, minority (Native American) students, this project prepares a more diversified workforce in the area of biomedical engineering for the State of Oklahoma and the nation.

该项目开发了一个无处不在的人类健康监测系统，不仅可以收集生命体征，还可以收集人类受试者在日常生活环境中的日常活动和环境背景。从这些收集的数据中，提取出行为和生命体征异常等高级知识，以协助健康评估、医疗诊断/预后或医疗保健服务。研究小组首先开发了一种低成本、功耗敏感的可穿戴智能健康监测（SmartMon）系统，该系统集成在人体服装中，对穿着者的干扰最小。然后，研究小组采用了一种极简主义的方法来理解人类？使用SmartMon系统收集的数据，来分析学生的日常活动和环境背景。研究小组进一步发展自适应和智能信号处理算法，以检测人类日常活动和生命体征的异常，用于健康评估应用。该研究可显著提高可穿戴式人体健康监测的普遍性、准确性和可靠性。在这个项目中开发的技术有许多不同的应用，从改善老年人的生活质量到在紧急情况下提高急救人员的能力。通过女性，少数民族（美洲原住民）学生的参与，该项目为俄克拉荷马州和国家的生物医学工程领域准备了更多元化的劳动力。