SCH: INT: Collaborative Research: A Data-Driven Approach to Enhancing Wearable Device Performance - An Early Asthma Exacerbation Detection Study

SCH：INT：协作研究：增强可穿戴设备性能的数据驱动方法 - 早期哮喘恶化检测研究

• 批准号: 1915169
• 负责人: Michelle Hernandez
• 金额: $ 33.3万
• 依托单位: University of North Carolina at Chapel Hill
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915169&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能够连续感知许多生理参数，如心率、心率变异性、呼吸频率、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，以实现对严重症状的有效管理和预防。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，在不受约束的现实世界条件下保持可靠。因此，目前评估哮喘相关可穿戴设备的大多数程序通常是在受控环境中进行的，并且不能捕获个人日常使用期间设备可能暴露的广泛场景。这些现实场景可能会影响设备的数据质量和有用性。在这个项目中，研究人员旨在提供一个创新的框架，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，并旨在通过在年轻人中更可靠地早期检测哮喘发作来证明该框架的价值。该奖项产生的数据将用作本科生和研究生项目的一部分。将制作示范和视频材料，作为向K-12和代表性不足的社区推广工作的一部分。研究人员计划通过三个研究重点来实现他们的科学目标。(1)信号质量表征：将开发一个健壮的统计框架，根据实际使用的环境来表征信号质量。上下文将使用活动、环境和设备状态信息来表示。该项目将开发一种使用受控实验室实验的监督方法，并将框架扩展为无监督/半监督，以便适用于现实世界的条件。(2)建立早期哮喘加重的信号质量和上下文感知推理模型：信号质量的表征将用于开发更可靠的推理管道。(3)对用户和设备的反馈：对于推断和设备需要的任何调整，将向用户提供易于理解和可操作的反馈。这种反馈对信号质量和用户满意度的影响将被研究。该设备还将接收与采样和滤波相关的参数设置形式的反馈，以确保准确的预测水平，同时最大限度地降低功率分布。该奖项反映了美国国家科学基金会的法定使命，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

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

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

Robust Cough Detection With Out-of-Distribution Detection

• DOI: 10.1109/jbhi.2023.3264783
• 发表时间: 2023-04
• 期刊: IEEE Journal of Biomedical and Health Informatics
• 影响因子: 7.7
• 作者: Yuhan Chen;Pankaj Attri;J. Barahona;M. Hernandez;D. Carpenter;A. Bozkurt;E. Lobaton