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Jegan R, Nimi WS. On the development of low power wearable devices for assessment of physiological vital parameters: a systematic review. ZEITSCHRIFT FUR GESUNDHEITSWISSENSCHAFTEN = JOURNAL OF PUBLIC HEALTH 2023:1-16. [PMID: 37361281 PMCID: PMC10068243 DOI: 10.1007/s10389-023-01893-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 03/14/2023] [Indexed: 04/05/2023]
Abstract
Aim Smart wearable devices for continuous monitoring of health conditions have bbecome very important in the healthcare sector to acquire and assess the different physiological parameters. This paper reviews the nature of physiological signals, desired vital parameters, role of smart wearable devices, choices of wearable devices and design considerations for wearable devices for early detection of health conditions. Subject and methods This article provides designers with information to identify and develop smart wearable devices based on the data extracted from a literature survey on previously published research articles in the field of wearable devices for monitoring vital parameters. Results The key information available in this article indicates that quality signal acquisition, processing and longtime monitoring of vital parameters requires smart wearable devices. The development of smart wearable devices with the listed design criteria supports the developer to design a low power wearable device for continuous monitoring of patient health conditions. Conclusion The wide range of information gathered from the review indicates that there is a huge demand for smart wearable devices for monitoring health conditions at home. It further supports tracking heath status in the long term via monitoring the vital parameters with the support of wireless communication principles.
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Affiliation(s)
- R. Jegan
- Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - W. S. Nimi
- Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
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Kweon SJ, Rafi AK, Cheon SI, Je M, Ha S. On-Chip Sinusoidal Signal Generators for Electrical Impedance Spectroscopy: Methodological Review. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:337-360. [PMID: 35482701 DOI: 10.1109/tbcas.2022.3171163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This paper reviews architectures and circuit implementations of on-chip sinusoidal signal generators (SSGs) for electrical impedance spectroscopy (EIS) applications. In recent years, there have been increasing interests in on-chip EIS systems, which measure a target material's impedance spectrum over a frequency range. The on-chip implementation allows EIS systems to have low power and small form factor, enabling various biomedical applications. One of the key building blocks of on-chip EIS systems is on-chip SSG, which determines the frequency range and the analysis precision of the whole EIS system. On-chip SSGs are generally required to have high linearity, wide frequency range, and high power and area efficiency. They are typically composed of three stages in general: waveform generation, linearity enhancement, and current injection. First, a sinusoidal waveform should be generated in SSGs. The generated waveform's frequency should be accurately adjustable over a wide range. The firstly generated waveform may not be perfectly linear, including unwanted harmonics. In the following linearity-enhancement step, these harmonics are attenuated by using filters typically. As the linearity of the waveform is improved, the precision of the EIS system gets ensured. Lastly, the filtered voltage waveform is now converted to a current by a current driver. Then, the current sinusoidal signal is injected into the target impedance. This review discusses the principles, advantages, and disadvantages of various techniques applied to each step in state-of-the-art on-chip SSGs. In addition, state-of-the-art designs are compared and summarized.
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High Density Resistive Array Readout System for Wearable Electronics. SENSORS 2022; 22:s22051878. [PMID: 35271023 PMCID: PMC8914777 DOI: 10.3390/s22051878] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 02/15/2022] [Accepted: 02/25/2022] [Indexed: 12/10/2022]
Abstract
This work presents a wearable sensing system for high-density resistive array readout. The system comprising readout electronics for a high-density resistive sensor array and a rechargeable battery, was realized in a wristband. The analyzed data with the proposed system can be visualized using a custom graphical user interface (GUI) developed in a personal computer (PC) through a universal serial bus (USB) and using an Android app in smartphones via Bluetooth Low Energy (BLE), respectively. The readout electronics were implemented on a printed circuit board (PCB) and had a compact dimension of 3 cm × 3 cm. It was designed to measure the resistive sensor with a dynamic range of 1 KΩ–1 MΩ and detect a 0.1% change of the base resistance. The system operated at a 5 V supply voltage, and the overall system power consumption was 95 mW. The readout circuit employed a resistance-to-voltage (R-V) conversion topology using a 16-bit analog-to-digital converter (ADC), integrated in the Cypress Programmable System-on-Chip (PSoC®) 5LP microcontroller. The device behaves as a universal-type sensing system that can be interfaced with a wide variety of resistive sensors, including chemiresistors, piezoresistors, and thermoelectric sensors, whose resistance variations fall in the target measurement range of 1 KΩ–1 MΩ. The system performance was tested with a 60-resistor array and showed a satisfactory accuracy, with a worst-case error rate up to 2.5%. The developed sensing system shows promising results for applications in the field of the Internet of things (IoT), point-of-care testing (PoCT), and low-cost wearable devices.
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Li WD, Ke K, Jia J, Pu JH, Zhao X, Bao RY, Liu ZY, Bai L, Zhang K, Yang MB, Yang W. Recent Advances in Multiresponsive Flexible Sensors towards E-skin: A Delicate Design for Versatile Sensing. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2103734. [PMID: 34825473 DOI: 10.1002/smll.202103734] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 09/16/2021] [Indexed: 05/07/2023]
Abstract
Multiresponsive flexile sensors with strain, temperature, humidity, and other sensing abilities serving as real electronic skin (e-skin) have manifested great application potential in flexible electronics, artificial intelligence (AI), and Internet of Things (IoT). Although numerous flexible sensors with sole sensing function have already been reported since the concept of e-skin, that mimics the sensing features of human skin, was proposed about a decade ago, the ones with more sensing capacities as new emergences are urgently demanded. However, highly integrated and highly sensitive flexible sensors with multiresponsive functions are becoming a big thrust for the detection of human body motions, physiological signals (e.g., skin temperature, blood pressure, electrocardiograms (ECG), electromyograms (EMG), sweat, etc.) and environmental stimuli (e.g., light, magnetic field, volatile organic compounds (VOCs)), which are vital to real-time and all-round human health monitoring and management. Herein, this review summarizes the design, manufacturing, and application of multiresponsive flexible sensors and presents the future challenges of fabricating these sensors for the next-generation e-skin and wearable electronics.
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Affiliation(s)
- Wu-Di Li
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Kai Ke
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Jin Jia
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Jun-Hong Pu
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Xing Zhao
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Rui-Ying Bao
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Zheng-Ying Liu
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Lu Bai
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Kai Zhang
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Ming-Bo Yang
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
| | - Wei Yang
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu, Sichuan, 610065, China
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DellrAgnola F, Pale U, Marino R, Arza A, Atienza D. MBioTracker: Multimodal Self-Aware Bio-Monitoring Wearable System for Online Workload Detection. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:994-1007. [PMID: 34495839 DOI: 10.1109/tbcas.2021.3110317] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Cognitive workload affects operators' performance principally in high-risk or time-demanding situations and when multitasking is required. An online cognitive workload monitoring system can provide valuable inputs to decision-making instances, such as the operator's state of mind and resulting performance. Therefore, it can allow potential adaptive support to the operator. This work presents a new design of a wearable embedded system for online cognitive workload monitoring. This new wearable system consists of, on the hardware side, a multi-channel physiological signals acquisition (respiration cycles, heart rate, skin temperature, and pulse waveform) and a low-power processing platform. Further, on the software side, our wearable embedded system includes a novel energy-aware bio-signal processing algorithm. We also use the concept of application self-awareness to enable energy-scalable embedded machine learning algorithms and methods for online subjects' cognitive workload monitoring. Our results show that this new wearable system can continuously monitor multiple bio-signals, compute their key features, and provide reliable detection of high and low cognitive workload levels with a time resolution of 1 minute and a battery lifetime of 14.58 h in our experimental conditions. It achieves a detection accuracy of 76.6% (2.6% lower than analogous offline computer-based analysis) with a sensitivity of 77.04% and a specificity of 81.75%, on a simulated drone rescue mission task. Moreover, by applying our self-aware monitoring to exploit different energy-scalable modes, we can increase battery lifetime by 51.6% (up to 22.11 hours) while incurring an insignificant accuracy loss of 1.07%.
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Zhang L, Zhang S, Wang C, Zhou Q, Zhang H, Pan GB. Highly Sensitive Capacitive Flexible Pressure Sensor Based on a High-Permittivity MXene Nanocomposite and 3D Network Electrode for Wearable Electronics. ACS Sens 2021; 6:2630-2641. [PMID: 34228442 DOI: 10.1021/acssensors.1c00484] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
With the fast development of consumer electronic and artificial intelligence equipment, flexible pressure sensors (FPSs) have become a momentous component in the application of wearable electronic, electronic skin, and human-machine interfacing. The capacitive FPS possesses the merits of low energy consumption, high resolution, and fast dynamic response, so it is ideal for mobile and wearable electronics. However, capacitive FPS is vulnerable to electromagnetic interference and parasitic capacitance due to its low sensitivity. Microstructure or porous dielectric materials have been applied to improve the sensitivity of the capacitive FPS, but the high sensitivity is just limited to a narrow region. In this work, we propose a different strategy that incorporates a high-permittivity MXene nanocomposite dielectric with a 3D network electrode (3DNE) to improve the sensing performance of the capacitive FPS. Thanks to the high permittivity of the dielectric layer and hierarchical deformation of the electrode, the fabricated capacitive FPS exhibits a high sensitivity of 10.2 kPa-1 in the low pressure range (0-8.6 kPa) and still maintains a relatively high sensitivity of 3.65 kPa-1 with a near-linear response in a wide pressure range (8.6-100 kPa). In addition, the capacitive FPS can withstand over 20,000 times pressure loads without significant signal damping. Furthermore, the working mechanism of the capacitive FPS is illustrated by the finite element analysis (FEA) method and theoretical calculation. The application potential of the sensor in wearable electronics was demonstrated by human pulse wave monitoring and pressure mapping tests with a 4 × 6 sensor microarray.
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Affiliation(s)
- Long Zhang
- Division of Interdisciplinary and Comprehensive Research, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398, Ruoshui Road, Industrial Park, Suzhou 215123, P.R. China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 96, Jinzhai Road, Baohe District, Hefei 230026, P.R. China
| | - Shaohui Zhang
- Division of Interdisciplinary and Comprehensive Research, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398, Ruoshui Road, Industrial Park, Suzhou 215123, P.R. China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 96, Jinzhai Road, Baohe District, Hefei 230026, P.R. China
| | - Chao Wang
- Division of Interdisciplinary and Comprehensive Research, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398, Ruoshui Road, Industrial Park, Suzhou 215123, P.R. China
| | - Quan Zhou
- Division of Interdisciplinary and Comprehensive Research, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398, Ruoshui Road, Industrial Park, Suzhou 215123, P.R. China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 96, Jinzhai Road, Baohe District, Hefei 230026, P.R. China
| | - Haifeng Zhang
- Division of Interdisciplinary and Comprehensive Research, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398, Ruoshui Road, Industrial Park, Suzhou 215123, P.R. China
| | - Ge-Bo Pan
- Division of Interdisciplinary and Comprehensive Research, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, No. 398, Ruoshui Road, Industrial Park, Suzhou 215123, P.R. China
- School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, No. 96, Jinzhai Road, Baohe District, Hefei 230026, P.R. China
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Kubicek J, Fiedorova K, Vilimek D, Cerny M, Penhaker M, Janura M, Rosicky J. Recent Trends, Construction and Applications of Smart Textiles and Clothing for Monitoring of Health Activity: A Comprehensive Multidisciplinary Review. IEEE Rev Biomed Eng 2020; 15:36-60. [PMID: 33301410 DOI: 10.1109/rbme.2020.3043623] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In the area of biomedical signal monitoring, wearable electronics represents a dynamically growing field with a significant impact on the market of commercial products of biomedical signal monitoring and acquisition, as well as consumer electronic for vital functions monitoring. Since the electrodes are perceived as one of the most important part of the biomedical signal monitoring, they have been one of the most frequent subjects in the research community. Electronic textile (e-textile), also called smart textile represents a modern trend in the wearable electronics, integrating of functional materials with common clothing with the goal to realize the devices, which include sensors, antennas, energy harvesters and advanced textiles for self-cooling and heating. The area of textile electrodes and e-textile is perceived as a multidisciplinary field, integrating material engineering, chemistry, and biomedical engineering. In this review, we provide a comprehensive view on this area. This multidisciplinary review integrates the e-textile characteristics, materials and manufacturing of the textile electrodes, noise influence on the e-textiles performance, and mainly applications of the textile electrodes for biomedical signal monitoring and acquisition, including pressure sensors, electrocardiography, electromyography, electroencephalography and electrooculography monitoring.
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