Data Acquisition Systems for the Internet of Medical Things

数据采集系统（DAS）将从传感器收集的模拟信号转换为数字值，并将其传送给计算机，用特定的算法对其进行处理。连接到网络中的智能DAS可以实现网络物理系统（CPS）来解决复杂问题。当进一步配备无线连接时，这些设备可以构成智能传感器网络（SSN）的节点，甚至可以集成到物联网（IoT）中。物联网在制造过程自动化、智能家居/城市/电网/供应链/农业和联网车辆等广泛领域显示出显著的扩散。物联网的发展主要基于计算机网络、电信和软件算法方面的知识。尽管物联网被认为是解决世界人口老龄化的许多医疗保健问题的“银弹”，但医疗物联网（IoMT）的发展速度仍然比其他应用慢得多。造成这种减速的因素被认为与安全性、安全性和健壮性以及验证和验证程序有关。目前的研究旨在从计算机和网络层面解决这些问题，而忽略了DAS的局限性。IoMT最具挑战性的应用之一是个人健康监测。生理参数的评估依赖于与这些参数相关的生物信号或替代物的测量，这些信号或替代物是无创获得的。尽管可穿戴设备代表了最大的物联网市场之一，但所使用的DAS精度不高，是物联网在健康监测领域普及的一个重要障碍。从业人员对IoMT的低信心是基于对所获得信号的质量、患者对医嘱的遵守情况（包括进行这些测量的条件）和医疗设备的状态缺乏信息。为了提高IoMT收集信息的准确性、可信度和充分性，在本研究计划的框架内，将构思新的DAS架构、CPS模型、算法和电路。将定义一组测量质量保证（MQA）参数，以客观地表征测量、测量不确定度、采集条件以及测量设备和算法。DAS优化方法将考虑几个正交标准，如相对量化误差、估计和间接测量算法的不确定性、MQA指标和功耗。该项目旨在通过使从业人员能够获得可靠的数据，提高加拿大医疗保健的质量。扩大后的IoMT将支持精准医疗方法的发展，而政府组织将能够评估其政策的效率。

Data Acquisition Systems (DAS) convert analog signals, collected from sensors, to digital values that are delivered to computers to process them with specific algorithms. Smart DAS's connected in a network can implement a Cyber-Physical System (CPS) to address complex problems. When further equipped with wireless connectivity, such devices can constitute nodes of smart sensor networks (SSN), or even can be integrated in Internet of Things (IoT). IoT has shown a significant proliferation in a broad range of domains, such as manufacturing processes automation, smart homes / cities / grids / supply chain / farming, and connected vehicles. IoT has evolved mainly based on knowledge in computer networks, telecommunications and software algorithms. Even though the IoT is considered a "silver bullet" which would solve many of the healthcare problems of the world aging population, still, the development of the Internet of Medical Things (IoMT) has shown a considerable slower pace than the other applications. The factors that are considered responsible for this slowdown are related to safety, security and robustness, along with verification and validation procedures. Current researches aim to address these problems at the computers and network levels, while overlooking DAS limitations. One of the most challenging applications of IoMT is personal health monitoring. Assessment of physiological parameters relies on measurement of bio-signals or surrogates that are correlated with these parameters and which are acquired non-invasively. Even though wearable devices represent one of the largest IoT markets, the modest precision of the employed DAS represent an important impediment in the proliferation of IoMT in health monitoring. The practitioners' low confidence in the IoMT is based on the lack of information on the quality of the acquired signals, patients' adherence to doctors' orders (including conditions in which these measurements are performed) and the state of the medical devices. In order to increase accuracy, trustworthiness and adequacy of the collected information by IoMT, in the frame of this research program, novel DAS architectures, CPS models, algorithms and circuits will be conceived. A set of Measurement Quality Assurance (MQA) parameters will be defined to objectively characterize the measurands, measurement uncertainty, acquisition conditions, and measuring devices and algorithms. DAS optimization methods will consider several orthogonal criteria, such as, relative quantization error, uncertainty of estimation and indirect measurement algorithms, MQA metrics, and power consumption. This project aims at improving the quality of healthcare in Canada by enabling practitioners to have access to trustworthy data. The augmented IoMT will support development of precision medicine methods, while governmental organizations will be able to evaluate the efficiency of their policies.