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

可穿戴设备的进步使人们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，广泛采用可穿戴设备存在重大挑战，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结果，而可穿戴技术的目标是集成到日常生活中，并在不受限制的现实世界条件下可靠。因此，目前评估哮喘相关可穿戴设备的大多数程序通常发生在受控环境中，没有涵盖设备在个人日常使用过程中可能暴露的广泛场景。这些现实世界的场景可能会影响设备的数据质量和可用性。在这个项目中，研究人员的目标是提供一个创新的框架，根据可穿戴设备使用的上下文信息来表征其在现实世界中的性能，并旨在通过使年轻人能够更可靠地及早发现哮喘恶化来展示该框架的价值。该奖项产生的数据将作为本科生和研究生项目的一部分。将制作演示和视频材料，作为针对K-12和代表性不足社区的外联工作的一部分。调查人员计划通过专注于三个研究项目来实现他们的科学目标。(1)信号质量的表征：将制定一个强有力的统计框架，根据信号质量的使用情况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，以便适用于真实世界的条件。(2)开发哮喘早期加重的信号质量和上下文感知推理模型：信号质量的表征将被用于开发更可靠的推理管道。(3)对用户和设备的反馈：将为用户提供易于理解和可操作的关于推断和设备所需调整的反馈。将研究这种反馈对信号质量和用户满意度的影响。该设备还将以与采样和过滤相关的参数设置的形式接收反馈，这将确保准确的预测水平，同时将功率分布降至最低。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。

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.