Grant: Discovery Grants Program - Individual

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.