Graphene textile smart clothing for wearable cardiac monitoring

Wearable electronics is a rapidly growing field that recently started to introduce successful commercial products into the consumer electronics market. Employment of biopotential signals in wearable systems as either biofeedbacks or control commands are expected to revolutionize many technologies including point of care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMIs), and brain–computer interfaces (BCIs). Since electrodes are regarded as a decisive part of such products, they have been studied for almost a decade now, resulting in the emergence of textile electrodes. This study reports on the synthesis and application of graphene nanotextiles for the development of wearable electrocardiography (ECG) sensors for personalized health monitoring applications. In this study, we show for the first time that the electrocardiogram was successfully obtained with graphene textiles placed on a single arm. The use of only one elastic armband, and an “all-textile-approach” facilitates seamless heart monitoring with maximum comfort to the wearer. The functionality of graphene textiles produced using dip coating and stencil printing techniques has been demonstrated by the non-invasive measurement of ECG signals, up to 98% excellent correlation with conventional pre-gelled, wet, silver/silver-chloride (Ag / AgCl) electrodes. Heart rate have been successfully determined with ECG signals obtained in different situations. The system-level integration and holistic design approach presented here will be effective for developing the latest technology in wearable heart monitoring devices

Physiological Parameter Sensing with Wearable Devices and Non-Contact Dopper Radar.

M.S. Thesis. University of Hawaiʻi at Mānoa 2017

Ultra-Low Power on Skin ECG using RFID Communication

Electrocardiograms provide rhythm, rate and electrical activity of the heart which can be used to diagnose health issues. Current methodologies for wireless based heart monitoring favour the use of Bluetooth Low Energy, which can require bulky batteries for device longevity. This paper investigates the use of a novel ultra-low power communications technique utilising Ultra High Frequency Radio Frequency Identification to stream ECG data in real time to a host computer to enable sub 2mW power consumption

A Hybrid-Powered Wireless System for Multiple Biopotential Monitoring

Chronic diseases are the top cause of human death in the United States and worldwide. A huge amount of healthcare costs is spent on chronic diseases every year. The high medical cost on these chronic diseases facilitates the transformation from in-hospital to out-of-hospital healthcare. The out-of-hospital scenarios require comfortability and mobility along with quality healthcare. Wearable electronics for well-being management provide good solutions for out-of-hospital healthcare. Long-term health monitoring is a practical and effective way in healthcare to prevent and diagnose chronic diseases. Wearable devices for long-term biopotential monitoring are impressive trends for out-of-hospital health monitoring. The biopotential signals in long-term monitoring provide essential information for various human physiological conditions and are usually used for chronic diseases diagnosis. This study aims to develop a hybrid-powered wireless wearable system for long-term monitoring of multiple biopotentials. For the biopotential monitoring, the non-contact electrodes are deployed in the wireless wearable system to provide high-level comfortability and flexibility for daily use. For providing the hybrid power, an alternative mechanism to harvest human motion energy, triboelectric energy harvesting, has been applied along with the battery to supply energy for long-term monitoring. For power management, an SSHI rectifying strategy associated with triboelectric energy harvester design has been proposed to provide a new perspective on designing TEHs by considering their capacitance concurrently. Multiple biopotentials, including ECG, EMG, and EEG, have been monitored to validate the performance of the wireless wearable system. With the investigations and studies in this project, the wearable system for biopotential monitoring will be more practical and can be applied in the real-life scenarios to increase the economic benefits for the health-related wearable devices

Innovative Wearable Sensors Based on Hybrid Materials for Real-Time Breath Monitoring

This chapter will present the importance of innovative hybrid materials for the development of a new generation of wearable sensors and the high impact on improving patient’s health care. Suitable conductive nanoparticles when embedded into a polymeric or glass host matrix enable the fabrication of flexible sensor capable to perform automatic monitoring of human vital signs. Breath is a key vital sign, and its continuous monitoring is very important including the detection of sleep apnea. Many research groups work to develop wearable devices capable to monitor continuously breathing activity in different conditions. The tendency of integrating wearable sensors into garment is becoming more popular. The main reason is because textile is surrounding us 7 days a week and 24 h a day, and it is easy to use by the wearer without interrupting their daily activities. Technologies based on contact/noncontact and textile sensors for breath detection are addressed in this chapter. New technology based on multi-material fiber antenna opens the door to future methods of noninvasive and flexible sensor network for real-time breath monitoring. This technology will be presented in all its aspects

Arrhythmia Classification Algorithm Based on Multi-Feature and Multi-type Optimized SVM

The electrocardiogram (ECG) signal feature extraction and classification diagnosis algorithm is proposed to address the high incidence of heart disease and difficulty in self-detection. First, the collected ECG signals are preprocessed to remove the noise of the ECG signals. Next, wavelet packet decomposition is used to perform a four-layer transformation on the denoised ECG signal and the 16 obtained wavelet packet coefficients analyzed statistically. Next, the slope threshold method is used to extract the R-peak of the denoised ECG signal. The RR interval can be calculated according to the extracted R peak. The extracted statistical features and time domain RR interval features are combined into a multi-domain feature space. Finally, the particle swarm optimization algorithm (PSO), genetic algorithm (GA), and grid search (GS) algorithms are applied to optimize the support vector machine (SVM). The optimized SVM is utilized to classify the extracted multi-domain features. Classification results show the proposed algorithm can classify six types of ECG beats accurately. The classification efficiency achieved by PSO, GA, and GS are 97.78%, 98.33%, and 98.89%, respectively

A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.

Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features

The use of IoT technology is rapidly increasing in healthcare development and smart healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency of monitoring, various studies have been conducted in this field to achieve improved precision. The architecture proposed herein is based on IoT integrated with a cloud system in which power absorption and accuracy are major concerns. We discuss and analyze development in this domain to improve the performance of IoT systems related to health care. Standards of communication for IoT data transmission and reception can help to understand the exact power absorption in different devices to achieve improved performance for healthcare development. We also systematically analyze the use of IoT in healthcare systems using cloud features, as well as the performance and limitations of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of various healthcare issues in elderly people and limitations of an existing system in terms of resources, power absorption and security when implemented in different devices as per requirements. Blood pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications of NB-IoT (narrowband IoT), technology that supports widespread communication with a very low data cost and minimum processing complexity and battery lifespan. This article also focuses on analysis of the performance of narrowband IoT in terms of delay and throughput using singleand multinode approaches. We performed analysis using the message queuing telemetry transport protocol (MQTTP), which was found to be efficient compared to the limited application protocol (LAP) in sending information from sensors.Ministerio Español de Ciencia e Innovación under project number PID2020-115570GB-C22 (DemocratAI::UGR)Cátedra de Empresa Tecnología para las Personas (UGR-Fujitsu