EAGER: Methodologies for Tight Integration of Physical and Cyber Models in Power Aware Wearable Computers

EAGER：在功率感知可穿戴计算机中紧密集成物理模型和网络模型的方法

• 批准号: 1138396
• 负责人: Roozbeh Jafari
• 金额: $ 5.26万
• 依托单位: University of Texas at Dallas
• 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: 2011-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1138396&HistoricalAwards=false
• 关键词: EAGER; Methodologies; Tight; Integration; Physical

Wearable computers are gaining significant attention due to their capability to enable a wide variety of new applications in domains such as wellness and health care. Despite their tremendous potential to impact our lives, wearable health monitoring systems face a number of hurdles before becoming a reality. The enabling processors and architectures demand a large amount of energy, requiring sizable batteries. This creates challenges for further miniaturization of the wearable units. This EAGER award is pursuing preliminary research in tiered, model based signal processing that can exploit pre-determined signal templates to enable extreme power optimization. In this approach, signal processing can be performed at several levels, where in each level, only the hardware for a specific template is active. If the template of interest is present, the next level of signal processing will be activated, otherwise hardware components corresponding to the next and the remaining levels will remain inactive. This approach, however, highly depends on the effectiveness of templates. In monitoring human movements, if templates do not accurately represent the physical activity of interest, the system will not exhibit acceptable accuracy. The goal is to develop effective techniques and methodologies to ensure templates adapt to remain valid throughout the operation of the system, accurately representing the corresponding physical movements.The research focuses on speed-insensitive template matching architectures that can reduce the effects of movement variations on signal processing. Timing models for movements and user activity profiles are exploited to monitor the correctness of the signal processing, and tunable parameters decrease or increase the sensitivity of the signal processing. For example, if the user is expected to perform sit to stand at least once every two hours in the day time, and the tiered signal processing has not detected the movement in the past few hours, the sensitivity will be increased, or user interaction and template retraining can be initiated. When performing a movement that has been determined to be of interest, the user can initiate (re)training if the system does not recognize the movement. Effective template generation and on-line retraining are expected to open opportunities to individualize systems and signal processing and to reduce the complexity of storage and processing architectures. This research is expected to provide the groundwork for ongoing design and development of practical ultra low power signal processing architectures, reduce costs of computing platforms for medical sensing, and to enable future progress in areas such as gait and balance monitoring for fall prevention, and in-home movement monitoring for Parkinson?s disease.

可穿戴计算机由于能够在健康和医疗保健等领域提供各种新应用，因此引起了极大的关注。尽管它们具有影响我们生活的巨大潜力，但可穿戴健康监测系统在成为现实之前仍面临许多障碍。启用处理器和体系结构需要大量能量，需要相当大的电池。这给可穿戴设备的进一步小型化带来了挑战。这项渴望的奖项是在基于模型的基于模型的信号处理中进行初步研究，该研究可以利用预定的信号模板以实现极端功率优化。在这种方法中，可以在每个级别上执行信号处理，在每个级别中，只有特定模板的硬件是活动的。如果存在感兴趣的模板，则将激活下一个信号处理级别，否则，与下一个相对应的硬件组件将保持不活跃。 但是，这种方法在很大程度上取决于模板的有效性。在监视人类运动时，如果模板不能准确代表感兴趣的体育活动，则系统将无法表现出可接受的准确性。目的是开发有效的技术和方法，以确保模板在系统的整个操作过程中都保持有效，从而准确地表示相应的物理运动。该研究重点是匹配速度不敏感的模板，匹配架构，以减少运动变化对信号处理的影响。利用运动和用户活动概况的定时模型来监视信号处理的正确性，可调参数会降低或增加信号处理的灵敏度。例如，如果预计用户在白天至少每两个小时执行坐姿至少站立一次，并且分层信号处理未检测到过去几个小时内的运动，则可以提高灵敏度，或者可以启动用户交互和模板再培训。 当执行确定要感兴趣的运动时，如果系统不识别运动，则用户可以启动（重新）培训。有效的模板生成和在线再培训有望为个性化系统和信号处理，并减少存储和处理架构的复杂性。预计这项研究将为持续的设计和开发实用的超低功率信号处理体系结构，降低计算平台的医学感应成本以及在诸如步态和平衡预防秋季预防跌落的领域以及帕克森病的家庭运动监测等领域的未来进展。