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.

在许多研究中，已证明动态健康监测为患者提供了显著的益处。 改善患者结果的最大希望在于对慢性病患者进行长期的动态监测，以帮助医生和患者在达到危机阶段之前发现健康问题。 该项目追求设计具有日常服装的外观和感觉的用于门诊健康监测的服装，以及通过自动注释活动和运动的生理数据来消除患者依从性的一些障碍的监测方法，仅在特定条件下收集数据，并且对日常生活的影响最小。 智能的优点在于算法，设计方法和评估方法，使穿戴和遗忘的服装，为门诊健康监测。 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算法进行活动分类。 该项目通过开发将基于纤维的传感器纳入服装的设计和评估方法，解决了提供外观和感觉像日常服装的服装的挑战。 与分立传感器相比，这些传感器具有显著的优势，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。 这项研究的更广泛影响在于有可能消除阻碍有效使用动态健康监测的障碍，以改善患者及其家属的生活质量。