SHB: Small: Collaborative Research: Electronic Textiles for Ambulatory Health Monitoring

SHB：小型：合作研究：用于动态健康监测的电子纺织品

• 批准号: 1116719
• 负责人: Lucy Dunne
• 金额: $ 19.2万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116719&HistoricalAwards=false
• 关键词: SHB; Small; Collaborative; Research; Electronic

Ambulatory health monitoring has been demonstrated to provide significant benefits to patients in a number of studies. The biggest promise for improved results for patients lies in the long-term, ambulatory monitoring of patients with chronic illnesses to help doctors and patients identify health issues before they reach a crisis stage. This project pursues the design of garments for ambulatory health monitoring that have the look and feel of every day clothing, together with an approach to monitoring that removes some of the barriers to patient compliance by automatically annotating physiological data with activities and motions, collecting data only during specific conditions, and having minimal impact on daily routine. The intellectual merits lie in the algorithms, design methodologies, and evaluation methodologies that enable wear-and-forget garments for ambulatory health monitoring. The approach developed by this project uses a computationally intensive pose estimation algorithm that automatically adapts a more computationally efficient algorithm for activity classification. This project addresses the challenges of providing garments that look and feel like everyday clothing by developing design and evaluation methodologies for incorporating fiber-based sensors into garments. These have significant advantages over discrete sensors by being woven or sewn into the fabric, covering much larger areas of a garment, and draping naturally. The broader impacts of the research lie in the potential to remove barriers that prevent the effective use of ambulatory health monitoring to improve the quality of life for patients and their families.

多项研究表明，动态健康监测可为患者带来显着益处。 改善患者治疗效果的最大希望在于对慢性病患者进行长期动态监测，以帮助医生和患者在达到危机阶段之前识别健康问题。 该项目致力于设计用于动态健康监测的服装，使其具有日常服装的外观和感觉，以及一种监测方法，通过自动注释活动和动作的生理数据，仅收集数据，消除患者依从性的一些障碍在特定条件下，对日常生活的影响最小。 其智力优势在于算法、设计方法和评估方法，使穿忘式服装能够用于动态健康监测。 该项目开发的方法使用计算密集型姿态估计算法，该算法自动适应计算效率更高的活动分类算法。 该项目通过开发将基于纤维的传感器整合到服装中的设计和评估方法来解决提供外观和感觉像日常服装的服装的挑战。 与离散传感器相比，它们具有显着的优势，因为它们被编织或缝制到织物中，覆盖服装的更大区域，并且自然悬垂。 该研究更广泛的影响在于有可能消除阻碍有效使用动态健康监测以改善患者及其家人的生活质量的障碍。