Data acquisition optimization for smart monitoring networks

智能监控网络的数据采集优化

• 批准号: RGPIN-2014-05827
• 负责人: Groza, Voicu
• 金额: $ 2.26万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=567413
• 关键词: Data; acquisition; optimization; smart; monitoring

Data Acquisition Systems (DAS) digitize analog signals in order to be digitally processed by embedded systems in monitoring or control applications. DAS are at the front-end of the chain of data processing and, as such, any quantization and sampling error that is input to the computing system may cause catastrophic failures in the correct interpretation of the acquired signals. Current DAS have no provisions for Measurement Quality Assurance (MQA) to certify in real-time the correctness and trustworthiness of measurements by providing metadata on the precision, accuracy, conditions and utility of measurements, and which are essential requirements for operation in remote unsupervised environments. The lack of this information made impossible the development of viable monitoring technologies for personal healthcare applications or constrained their proliferation in other domains. Losing data because of unreliable operation, due to scarce resources caused by unexpected events, fuelled a lack of confidence in these technologies, as well. In order to address the resources efficiency, reliability and versatility, while increasing accuracy, trustworthiness and adequacy of the collected information, we propose to conceiving, developing and optimizing architectures, algorithms and models at different levels of data acquisition. The quantization process will be marked by MQA qualifiers which will objectively and globally characterize the measurands (acquired signals), measurement uncertainty, acquisition conditions (compliance with the requirements of standards), and measuring devices (Hardware and Software), including the accuracy of the indirect measurement algorithms. This research will employ compressed sensing techniques in order to reduce digitization relative errors and optimize the density of transmitted data. Floating-point differential quantization methods will be used to insure a broad dynamic range of the acquired signals and eliminate redundant information. Adaptive sampling rate algorithms will be studied in the context of the acquisition of sparse signals with strong deterministic components, but which may occasionally deviate from stable operation. Adaptive compressive sampling and quantization will mitigate the power consumption if resources get scarcely, while maximizing the data acquisition capacity and minimizing the measurement uncertainty. One of the most challenging applications of DAS is the non-invasive personal health monitoring. Assessment of quotidian physiological parameters relies on measurement and processing of bio-signals that are strongly correlated with these parameters and which can be acquired non-invasively. Optimization of methods in estimation of physiological parameters based on multi-sensors and algorithms fusion will be a central objective of this research plan. The amount of collected data will be optimally adapted to the resources constraints. Current technologies require more power for data transmission rather than processing. Hence, the local DAS management will decide to calculate locally the parameters whenever is possible, to minimize communication, in order to save energy. This research project will be validated by implementing its findings on a multi-sensor DAS platform that will be capable of fusing data, adapting its operation and structure to the dynamics of the monitored signals, selftesting, self-calibrating and estimating the MQA parameters. This project aims at improving the quality of health services in Canada by enabling people to monitor and report their health conditions. Not only will medical practitioners have access to trustworthy data, but governmental agencies will also be able to concurrently monitor the efficiency of their policies and investments.

数据采集系统（DAS）将模拟信号数字化，以便在监测或控制应用中由嵌入式系统进行数字处理。DAS处于数据处理链的前端，因此，输入到计算系统的任何量化和采样错误都可能导致对所获取信号的正确解释出现灾难性故障。目前的DAS没有测量质量保证（MQA）的规定，通过提供有关测量的精度、准确性、条件和效用的元数据来实时证明测量的正确性和可信度，而这些是远程无监督环境中操作的基本要求。由于缺乏这方面的信息，就不可能为个人医疗保健应用开发可行的监测技术，或者限制了这些技术在其他领域的推广。由于操作不可靠而导致的数据丢失，以及由于意外事件导致的资源稀缺，也加剧了对这些技术的缺乏信心。为了解决资源效率、可靠性和通用性问题，同时提高收集信息的准确性、可信度和充分性，我们建议在不同的数据采集层面构思、开发和优化架构、算法和模型。量化过程将由MQA限定符标记，该限定符将客观和全面地表征测量（采集的信号）、测量不确定度、采集条件（符合标准要求）和测量设备（硬件和软件），包括间接测量算法的准确性。本研究将采用压缩感知技术，以减少数字化的相对误差和优化传输数据的密度。采用浮点微分量化方法，保证了采集信号的宽动态范围，消除了冗余信息。自适应采样率算法将在具有强确定性成分但偶尔可能偏离稳定运行的稀疏信号采集的背景下进行研究。自适应压缩采样和量化可以在资源有限的情况下降低功耗，同时最大限度地提高数据采集能力和减小测量不确定度。非侵入性个人健康监测是DAS最具挑战性的应用之一。日常生理参数的评估依赖于与这些参数密切相关的生物信号的测量和处理，这些信号可以无创地获得。基于多传感器和算法融合的生理参数估计方法的优化将是本研究计划的中心目标。收集的数据量将以最佳方式适应资源限制。目前的技术需要更多的能量来传输数据，而不是处理数据。因此，本地DAS管理将决定在可能的情况下在本地计算参数，以减少通信，以节省能源。该研究项目将通过在多传感器DAS平台上实施其研究结果进行验证，该平台将能够融合数据，使其操作和结构适应监测信号的动态，自我测试，自我校准和估计MQA参数。该项目旨在通过使人们能够监测和报告其健康状况，提高加拿大保健服务的质量。不仅医疗从业者可以获得可靠的数据，而且政府机构也将能够同时监测其政策和投资的效率。