权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

RAPID: SCH: Smart Wearable COVID19 BioTracker Necklace: Remote Assessment and Monitoring of Symptoms for Early Diagnosis, Continual Monitoring, and Prediction of Adverse Event

RAPID：SCH：智能可穿戴式 COVID19 BioTracker 项链：远程评估和症状监测，以实现早期诊断、持续监测和不良事件预测

基本信息

批准号：
2031594
负责人：
S Farokh Atashzar
金额：
$ 12.5万
依托单位：
New York University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-15 至 2022-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031594&HistoricalAwards=false
关键词：
RAPID SCH Smart Wearable COVID19

项目摘要

Due to the outbreak of the 2019 novel Coronavirus and the shock to the healthcare systems, it is critical to conduct fundamental research and scientific studies on the development of smart and scalable technologies that can be produced rapidly and in large scales for (a) continual in-home and in-hospital objective tracking of multiple symptoms using wearable sensors, (b) detecting earlyhealth anomalies from the measured multiple symptoms, and (c) predicting potential adverse events from the measured historical data. This project will address the aforementioned aims to transform the current subjective infrequent telemonitoring for COVID19 patients to continuous objective monitoring and help to reduce the burden on the health care system and the corresponding social and economic impacts. The successful development and deployment of the wearable device will further open doors to conducting fundamental research on the temporal evolution of COVID19 symptoms and behavior.The project will develop a wireless smart Internet of Things (IoT) necklace, containing multimodality sensors that can accurately and objectively track multiple vital symptoms of respiratory malfunction and infection, covering a large spectrum of COVID19 symptoms. It will be capable of recording and telereporting, the sound and vibration dynamics due to breathing and coughing together with the body temperature, heart rate, and fatigue-related mobility. In addition, peripheral oxygen saturation (SpO2) will be measured and fused with the collected data by the IoT necklace. The project will also develop novel machine learning models to detect early health anomalies in the multidimensional biosignal space and predict the upcoming adverse events over a future temporal horizon based on the past time-series captured by the device. By partnering with industry, this technology can be used not only for COVID-19 but also for patients with other causes of pneumonia and other respiratory conditions, including lung cancer. The project will generate a unique transdisciplinary educational environment by conducting workshops and undergraduate projects with a focus on promoting STEM-related activities within underrepresented groups.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于2019年新型冠状病毒的爆发和对医疗系统的冲击，对可快速和大规模生产的智能可扩展技术的开发进行基础研究和科学研究至关重要，以(A)使用可穿戴传感器对多种症状进行持续的家庭和医院内客观跟踪，(B)从测量的多个症状检测早期健康异常，以及(C)根据测量的历史数据预测潜在的不良事件。该项目将解决上述目标，将目前对COVID19患者的主观、不频繁的远程监测转变为持续的客观监测，并帮助减轻卫生保健系统的负担以及相应的社会和经济影响。可穿戴设备的成功开发和部署将进一步为开展COVID19症状和行为的时间演变的基础研究打开大门。该项目将开发一种无线智能物联网(IoT)项链，包含多模传感器，可以准确和客观地跟踪呼吸故障和感染的多种重要症状，覆盖广泛的COVID19症状。它将能够记录和远程报告由于呼吸和咳嗽而产生的声音和振动动力学，以及体温、心率和与疲劳相关的流动性。此外，还将测量周围血氧饱和度(SpO2)，并通过IoT项链与收集的数据进行融合。该项目还将开发新的机器学习模型，以检测多维生物信号空间中的早期健康异常，并根据设备捕获的过去时间序列预测未来时间范围内即将发生的不良事件。通过与业界合作，这项技术不仅可以用于新冠肺炎，还可以用于其他原因肺炎和其他呼吸系统疾病的患者，包括肺癌。该项目将通过举办研讨会和本科项目来创造一个独特的跨学科教育环境，重点是在代表性不足的群体中促进与STEM相关的活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Perilaryngeal-Cranial Functional Muscle Network Differentiates Vocal Tasks: A Multi-Channel sEMG Approach

喉周颅功能肌肉网络区分声音任务：多通道 sEMG 方法

DOI：
10.1109/tbme.2022.3175948
发表时间：
2022
期刊：
IEEE Transactions on Biomedical Engineering
影响因子：
4.6
作者：
O' Keeffe, Rory;Shirazi, Seyed Yahya;Mehrdad, Sarmad;Crosby, Tyler;Johnson, Aaron M.;Atashzar, S. Farokh
通讯作者：
Atashzar, S. Farokh

Energetic Passivity Decoding of Human Hip Joint for Physical Human-Robot Interaction