Testing the Feasibility of Batteryless Physiological Monitoring

该奖项研究如何监测最常见的生理变量（心率，血压，呼吸和大脑活动）与微型设备，可以从身体收集能量，而不是由电池供电，并使用电极，可以应用到皮肤作为“纹身”。下一代移动的健康（mHealth）设备将进一步改善国民健康和福祉。实现这一目标的一个主要瓶颈是如何降低从收集的信号中提取信息所需的算法的功耗。该项目的目的是设计、实现和验证一种新的超低功耗信号处理解决方案，该解决方案不需要数字计算机，而是由输入脉冲序列驱动的更简单的数字设备。该项目还将培养两名研究生，在理论和技术方面设计下一代生物医学设备。该合同将开发新的基于脉冲的算法和可重新配置的硬件平台，以真实的时间放大，转换和量化信号的结构。更具体地说，该研究计划包括两个协同目标：第一个目标是开发一个基于信号处理的数学框架和一种直接从数据中学习输入结构的语法-句法方法。将从数据中统计训练称为KAARMA（内核自适应自回归移动平均模型）的非线性状态模型，以识别具有临床意义的事件。一旦经过训练，KAARMA可以转换为有限状态机和内存表的组合，可以很容易地在超低功耗可重构数字逻辑平台中实现，以设计移动健康生理变量的动态监测。在所部署的建议设备中不需要数字信号处理器，从而降低功耗，保持可编程性和信息的数字提取质量。第二个目标是设计一种超低功耗可重构模拟前端传感集成电路，主要使用数字标准单元来实现可变通道数、多用途模拟放大和滤波以及有限状态机。预期目标是证明分析一个心电图（ECG）通道的功耗小于5微瓦。KAARMA将扩展到血压，呼吸和大脑活动。将在Physionet数据库中使用竞争技术进行确认。

This award studies the ways to monitor the most common physiological variables (heart rate, blood pressure, respiration and brain activity) with miniature devices that can harvest energy from the body instead of being powered by batteries, and using electrodes that can be applied to the skin as a "tattoo". This next generation of Mobile Health (mHealth) devices will improve further the national health and wellbeing. A major bottleneck towards the goal is how to decrease the power consumption of algorithms required to extract information from the collected signals. The aim of this project is to design, implement and validate a new ultra-low power signal processing solution that does not require digital computers, but much simpler digital devices driven by input pulse trains. The project will also train two graduate students in the theory and technology to design the next generation of biomedical devices. The award will develop new pulse based algorithms and a reconfigurable hardware platform that amplifies, converts and quantifies structure of the signals in real time. More specifically, the research plan includes two synergestic aims: the first aim develops a mathematical framework based on signal processing and a statistical-syntactic approach to learn directly from data the structure of the input. A nonlinear state model called KAARMA (kernel adaptive autoregressive moving average model) will be trained statistically from data to recognize events with clinical significance. Once trained, KAARMA can be converted in a combination of finite state machines and memory tables that can easily be implemented in ultra-low power reconfigurable digital logic platform to design ambulatory monitoring of physiological variables for mHealth. No digital signal processors are needed in the deployed proposed device, lowering power consumption, maintaining programmability and the quality of the digital extraction of information. The second aim is to design an ultra-low power reconfigurable analog front-end sensing integrated circuit using mainly digital standard cells to implement a variable number of channels, multipurpose analog amplification and filtering, and the finite state machines. The expected goal is to demonstrate power consumption of less than 5 microwatts to analyze one channel of electrocardiogram (ECG). The KAARMA will be extended to blood pressure, respiration and brain activity. Validation with competing technologies will be conducted in the Physionet database.