Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject’s body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

REVIEWbeing converged with artificial intelligence (AI) by providing users' biological and behavioral signals collected in wearable biodevices, [21][22][23][24][25][26][27][28][29][30][31] offering intelligent services based on big data cloud computing and machine learning. [32][33][34][35][36][37][38][39][40] A number of research groups have demonstrated flexible memories, thin film transistors (TFTs), and integrated circuits (ICs) as key technology for data processing, information storage, and communication. [41][42][43][44][45][46][47][48][49] Since Kim et al. [50] reported functional flexible resistive random access memory (RRAM), various chalcogenidebased phase change memories (PCMs) [51][52][53][54] as well as RRAMs using inorganic (e.g., WO 3 , Al 2 O 3 , HfO 2 , TiO 2 ), [45,49,50,[55][56][57] carbon (graphene, carbon nanotubes (CNTs)), [58][59][60][61][62][63][64] and organic materials [65][66][67][68] have been fabricated on polymer films. In addition, an ultrathin TFT and Si-based large-scale integration (LSI) were demonstrated for high-density flexible electronics. [8,69,70] Beyond the front-end device level, flexible packaging has been developed to interconnect core and peripheral modules to realize fully operational system-on-plastic (SoP). [71][72][73][74][75] Neuromorphic computing systems (brain-inspired model of parallel neuron network) are considered as a promising technology for AI applications, overcoming the limitation of von Neumann architecture (serial and iterative processing) for intelligent data analysis and low power consumption. [76][77][78][79][80][81] With the rapid advancement in the electronics (e.g., memristor, PCM, and TFT) on plastics or any type of surface, [82][83][84][85][86][87][88][89][90] emulation of biological synapses (adaptive synaptic weight, [91][92][93][94] and spike-timing-dependent plasticity (STDP) [95][96][97][98][99] of neurons) is being demonstrated on flexible substrate, which realizes an era of merged electronics toward cognitive IoT, physiological sensor, wearable computer, and autonomous driving system. [92,100] Here, we present recent progress in the field of electronics for flexible and neuromorphic applications that can be classified into four main categories: i) various devices (e.g., resistive memory, PCM, TFT, and IC) on plastic for computing, ii) flexible electronic systems using large-scale interconnection and packaging, iii) electronics for neuromorphic engineering, and iv) promising research areas of flexible synaptic applications. In addition, we have organized the main features of the studies in each section as a table to clearly compare their performance and challenges. The new electronic concept of a flexible and Emerging classes of flexible electronic systems that can be attached to a wide range of surfaces from wearable clothes to internal organs have driven significant advances in communication protocols (e.g., Internet of Things, augmented reality) and clinical research, shifting today's personal computing paradigm. The field of "system ...

Memristors are one of the emerging technologies that can potentially replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. Over the past few years, research and development mostly focused on revolutionizing the metal oxide materials, which are used as core components of the popular metal-insulator-metal memristors owing to their highly recognized resistive switching behavior. This paper outlines the recent advancements and characteristics of such memristive devices, with a special focus on (i) their established resistive switching mechanisms and (ii) the key challenges associated with their fabrication processes including the impeding criteria of material adaptation for the electrode, capping, and insulator component layers.Potential applications and an outlook into future development of metal oxide memristive devices are also outlined. Keywords: memory technology; memristor; RRAM; thin filmUsers without a subscription are not able to see the full content. Please, subscribe or login to access all content.

Artificial Intelligence (AI) at the edge has become a hot subject of the recent technology-minded publications. The challenges related to IoT nodes gave rise to research on efficient hardware-based accelerators. In this context, analog memristor devices are crucial elements to efficiently perform the multiply-and-add (MAD) operations found in many AI algorithms. This is due to the ability of memristor devices to perform in-memory-computing (IMC) in a way that mimics the synapses in human brain. Here, we present a novel planar analog memristor, namely NeuroMem, that includes a partially reduced Graphene Oxide (prGO) thin film. The analog and non-volatile resistance switching of NeuroMem enable tuning it to any value within the RON and ROFF range. These two features make NeuroMem a potential candidate for emerging IMC applications such as inference engine for AI systems. Moreover, the prGO thin film of the memristor is patterned on a flexible substrate of Cyclic Olefin Copolymer (COC) using standard microfabrication techniques. This provides new opportunities for simple, flexible, and cost-effective fabrication of solution-based Graphene-based memristors. In addition to providing detailed electrical characterization of the device, a crossbar of the technology has been fabricated to demonstrate its ability to implement IMC for MAD operations targeting fully connected layer of Artificial Neural Network. This work is the first to report on the great potential of this technology for AI inference application especially for edge devices.

This paper presents the design of a fully integrated electrocardiogram (ECG) signal processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier. Real-time and adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial points. Those techniques are robust to any variations in the ECG signal with high sensitivity and precision. Two databases of the heart signal recordings from the MIT PhysioNet and the American Heart Association were used as a validation set to evaluate the performance of the processor. Based on application-specified integrated circuit (ASIC) simulation results, the overall classification accuracy was found to be 86% on the out-of-sample validation data with 3-s window size. The architecture of the proposed ESP was implemented using 65-nm CMOS process. It occupied 0.112-mm 2 area and consumed 2.78-µW power at an operating frequency of 10 kHz and from an operating voltage of 1 V. It is worth mentioning that the proposed ESP is the first ASIC implementation of an ECG-based processor that is used for the prediction of ventricular arrhythmia up to 3 h before the onset.

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