SENSORS: Embedded Software for Distributed, Smart Sensor Networks

本研究的目的是解决在遥感的基本问题之一：信息的解释，并确定系统的状态，基于噪声和稀疏的数据从传感器网格源，与可中断的（有线或无线）通信信道互连。 这样的情况包括当传感器信号经常被传感器和身体之间的界面处的运动伪影支配时对走动受试者的医学感测。 本研究中探索的方法将传感器级信号处理与来自多个传感器的自适应最佳数据采样相结合，以减少/消除运动伪影，并确定整个测量和数据通信过程的可靠性/不确定性措施。 所提出的软件系统的非医疗应用，包括传感器控制和其他传感器网络的数据解释，如船上传感器，汽车传感器或飞机传感器网格的分布式，自主的传感器集成是必不可少的，在增加可靠性和操作safety.The本研究的目的是开发一种方法和软件，用于减少生物医学传感器的运动伪影。 该方法将使设备能够（a）融合来自多个联网的独立传感器的数据，以便导出关于感兴趣的信号的信息并表征干扰噪声，（B）使用自适应信号调节（自适应滤波、自动增益控制等）。和基于感兴趣的信号的特性的用于数据采集的最佳算法;以及（c）基于误差最小化的最佳采样标准提供数据质量和可靠性的定量测量。 运动伪影，即，由组织运动和传感器与身体表面的间歇接触引起的寄生信号在被测量的信号中引起显著的误差，并且是由走动的受试者使用的医学传感器中最严重和最难以消除的一类误差。 信息和通信技术成功地解决了许多远程保健监测问题;然而，远程医疗实施的成功取决于是否有非侵入性、最小接触、微型、自主和低功耗的处理器和嵌入智能传感器的软件，这些处理器和软件不受佩戴传感器的受试者的运动所造成的错误的影响。这项研究如果成功，将产生广泛的影响，能够开发出一类可靠的小型化个人远程保健监测设备和网络，供老年人、康复病人、在危险条件和环境下工作的人以及军事人员和运动员在训练中进行身体调节和在战场上进行保护监测。

This research is aimed at solving one of the fundamental problems in tele-sensing: the interpretation of information, and determination of the system state, based on noisy and sparse data from sensor grid sources, interconnected with interruptible (wired or wireless) communication channels. Such situations include medical sensing on ambulatory subjects when sensor signals are frequently dominated by motion artifacts at the interface between the sensor and the body. The approach explored in this research combines sensor-level signal processing with adaptive, optimal sampling of data from multiple sensors to reduce/eliminate motion artifacts and determine reliability/uncertainty measures for the entire measurement and data communication process. Non-medical applications of the proposed software system include sensor control and interpretation of data from other sensor networks, such as shipboard sensors, automotive sensors or airplane sensor grids where distributed, autonomous sensor ensembles are essential in increasing reliability and operational safety.The objective of this research is to develop a method and software for reducing motion artifacts in biomedical sensors. The method will enable devices that (a) fuse data from multiple, networked, independent sensors in order to derive information about the signal of interest and to characterize interfering noise, (b) use adaptive signal conditioning (adaptive filtering, automatic gain control, etc.) and Optimal Algorithms for data acquisition that are based on characteristics of the signal of interest; and (c) provide quantitative measures of data quality and reliability based on error-minimizing optimal sampling criteria. Motion artifacts, i.e., spurious signals caused by tissue movement and intermittent contact of the sensor with the body surface cause significant errors in the signal being measured and are the most serious and most difficult to eliminate class of errors in medical sensors for use by ambulatory subjects. Information and communication technologies successfully address many telehealth monitoring issues; however the success of telemedicine implementation depends on the availability of non-invasive, minimal-contact, miniature, autonomous and low power processors and software embedded in intelligent sensors, immune to errors caused by motion of the subject wearing them. This research, if successful, will have broad impact, enabling the development of a class of reliable, miniaturized personal telehealth monitoring devices and networks that will be used by the elderly persons, convalescent patients, persons working under hazardous conditions and environments, and by military personnel and athletes for physical conditioning in training and protective monitoring in the battlefield.