SCH: INT: Collaborative Research: A Data-Driven Approach for Enhancing Wearable Device Performance - A Study on Early Detection of Asthma Exacerbation

SCH：INT：协作研究：增强可穿戴设备性能的数据驱动方法 - 哮喘恶化早期检测的研究

• 批准号: 1915599
• 负责人: Edgar Lobaton
• 金额: $ 66.7万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915599&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Data

Advances on wearable devices have enabled the continuous sensing of a number of physiological parameters such as heart rate, heart rate variability, respiratory rate, activity levels, and coughing. These parameters can be used for a number of health applications, including prediction of asthma exacerbation, to achieve efficient management and prevention of severe symptoms. However, there have been significant challenges identified for the broad adoption of wearable devices, in particular ensuring reliable measurements and maximizing their battery-life. In current practice, clinical gold-standard devices can obtain reliable measurements in medical and controlled environments whereas wearable technologies target to be integrated into daily life and be reliable in unconstrained real-world conditions. As a result, most current procedures to evaluate asthma-related wearable devices often take place in controlled environments and do not capture the broad spectrum of scenarios that a device may be exposed to during an individual's daily use. These real-world scenarios can compromise data quality and usefulness of a device. In this project, the investigators aim to provide an innovative framework for characterizing the performance of wearable devices in the real-world based on contextual information of their usage, and aim to demonstrate the framework's value by enabling more reliable early detection of asthma exacerbations in young adults. The data produced by this award will be used as part of projects for undergraduate and graduate students. Demonstrations and video materials will be produced as part of the outreach efforts for K-12 and underrepresented communities.The investigators plan to achieve their scientific goals by focusing on three research thrusts. (1) Characterization of signal quality: A robust statistical framework will be developed to characterize signal quality in the real-world based on the context in which they are used. Context will be represented using activity, environmental and device-state information. The project will develop a supervised methodology using controlled in-lab experiments, and expand the framework to be unsupervised/ semi-supervised in order to be applicable to real-world conditions. (2) Development of a signal-quality and context-aware inference model for early asthma exacerbation: The characterization of signal quality will be used to develop more reliable inference pipelines. (3) Feedback to user and device: Users will be provided with easy-to-interpret and actionable feedback on the inference and any adjustments needed for the device. The effect of this feedback on signal quality and user satisfaction will be studied. The device will also receive feedback in the form of parameter settings associated with sampling and filtering that will ensure accurate levels of prediction while minimizing the power profile.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.

可穿戴设备的进步已经使得能够连续感测许多生理参数，诸如心率、心率变异性、呼吸率、活动水平和咳嗽。这些参数可用于许多健康应用，包括预测哮喘恶化，以实现有效管理和预防严重症状。然而，可穿戴设备的广泛采用面临着重大挑战，特别是确保可靠的测量和最大限度地延长电池寿命。在目前的实践中，临床黄金标准设备可以在医疗和受控环境中获得可靠的测量，而可穿戴技术的目标是融入日常生活，并在不受约束的现实世界条件下可靠。因此，评估哮喘相关可穿戴设备的大多数当前程序通常在受控环境中进行，并且不能捕获设备在个人日常使用期间可能暴露于的广泛场景。这些真实场景可能会损害设备的数据质量和实用性。在这个项目中，研究人员的目标是提供一个创新的框架，用于根据其使用的上下文信息来表征可穿戴设备在现实世界中的性能，并通过更可靠地早期检测年轻人的哮喘急性发作来证明该框架的价值。该奖项产生的数据将作为本科生和研究生项目的一部分。演示和视频材料将作为K-12和代表性不足的社区外展工作的一部分制作。研究人员计划通过专注于三个研究重点来实现他们的科学目标。(1)信号质量的表征：将开发一个强大的统计框架，以根据其使用的背景来表征现实世界中的信号质量。上下文将使用活动、环境和设备状态信息来表示。该项目将使用受控的实验室实验开发一种监督方法，并将框架扩展为无监督/半监督，以适用于现实世界的条件。(2)早期哮喘急性发作的信号质量和上下文感知推理模型的开发：信号质量的表征将用于开发更可靠的推理管道。(3)对用户和器械的反馈：将为用户提供关于推断和器械所需的任何调整的易于解释和可操作的反馈。将研究这种反馈对信号质量和用户满意度的影响。该设备还将接收与采样和过滤相关的参数设置形式的反馈，以确保准确的预测水平，同时最大限度地减少功率曲线。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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

Preliminary Assessment of Human Biological Responses to Low-level Ozone

人类对低浓度臭氧的生物反应的初步评估

• DOI: 10.1109/sensors47125.2020.9278620
• 发表时间: 2020
• 期刊: 2020 IEEE Sensors
• 作者: Latif, Tahmid;Gonzalez, Laura;Dieffenderfer, James;Liao, Yuwei;Hernandez, Michelle;Misra, Veena;Lobaton, Edgar;Bozkurt, Alper
• 通讯作者: Bozkurt, Alper

Investigating the Relationship between Cough Detection and Sampling Frequency for Wearable Devices

研究可穿戴设备的咳嗽检测与采样频率之间的关系

• DOI: 10.1109/embc46164.2021.9630082
• 发表时间: 2021
• 期刊: International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
• 作者: Abdelkhalek, Mahmoud;Qiu, Jinyi;Hernandez, Michelle;Bozkurt, Alper;Lobaton, Edgar
• 通讯作者: Lobaton, Edgar

Evaluation of Environmental Enclosures for Effective Ambient Ozone Sensing in Wrist-worn Health and Exposure Trackers

对腕戴式健康和暴露追踪器中有效环境臭氧传感的环境外壳进行评估

• DOI: 10.1109/sensors47087.2021.9639530
• 发表时间: 2021
• 期刊: IEEE SENSORS Conference 2021
• 作者: Latif, Tahmid;Dieffenderfer, James;Tanneeru, Akhilesh;Lee, Bongmook;Misra, Veena;Bozkurt, Alper
• 通讯作者: Bozkurt, Alper

Toward Automated Analysis of Fetal Phonocardiograms: Comparing Heartbeat Detection from Fetal Doppler and Digital Stethoscope Signals

胎儿心音图的自动分析：比较胎儿多普勒和数字听诊器信号的心跳检测

• DOI: 10.1109/embc46164.2021.9629814
• 发表时间: 2021
• 期刊: International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
• 作者: Chen, Yuhan;Wilkins, Michael D.;Barahona, Jeffrey;Rosenbaum, Alan J.;Daniele, Michael;Lobaton, Edgar
• 通讯作者: Lobaton, Edgar

Enhancing Inference on Physiological and Kinematic Periodic Signals via Phase-Based Interpretability and Multi-Task Learning

通过基于相位的可解释性和多任务学习增强对生理和运动周期信号的推理

• DOI: 10.3390/info13070326
• 发表时间: 2022
• 期刊: Information
• 影响因子: 3.1
• 作者: Soleimani, Reza;Lobaton, Edgar
• 通讯作者: Lobaton, Edgar