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

可穿戴计算机由于其能够在诸如健康和医疗保健的领域中实现各种各样的新应用的能力而获得显著关注。尽管可穿戴健康监测系统具有影响我们生活的巨大潜力，但在成为现实之前仍面临许多障碍。使能处理器和架构需要大量的能量，需要相当大的电池。这为可穿戴单元的进一步小型化带来了挑战。该EAGER奖项旨在对分层、基于模型的信号处理进行初步研究，这些信号处理可以利用预先确定的信号模板实现极端的功率优化。在这种方法中，信号处理可以在几个级别上执行，其中在每个级别中，只有用于特定模板的硬件是活动的。如果存在感兴趣的模板，则下一级信号处理将被激活，否则对应于下一级和剩余级的硬件组件将保持不活动。 然而，这种方法高度依赖于模板的有效性。在监测人体运动时，如果模板不能准确地表示感兴趣的身体活动，则系统将不会表现出可接受的准确性。目标是开发有效的技术和方法，以确保模板适应保持有效的整个系统的操作，准确地表示相应的物理movement.The研究的重点是速度不敏感的模板匹配架构，可以减少对信号处理的运动变化的影响。运动和用户活动配置文件的定时模型被用来监控信号处理的正确性，并且可调参数降低或增加信号处理的灵敏度。例如，如果预期用户在白天每两个小时至少执行一次坐立，并且分层信号处理在过去几个小时内没有检测到移动，则灵敏度将增加，或者可以发起用户交互和模板再训练。 当执行已确定为感兴趣的运动时，如果系统未识别该运动，则用户可以启动（重新）训练。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提供机会，并降低存储和处理架构的复杂性。这项研究预计将提供正在进行的实际超低功耗信号处理架构的设计和开发的基础，降低医疗传感的计算平台的成本，并使未来的进展，如步态和平衡监测跌倒预防等领域，并在家中运动监测帕金森？的疾病。

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.