Wearable sensors and devices for real-time cardiovascular disease monitoring

Lin, Jian; Fu, Rumin; Zhong, X L; Yu, Peng; Tan, Guoxin; Li, Wei; Zhang, Huan; Li, Yangfan; Zhou, Lei; Ning, Chengyun

doi:10.1016/j.xcrp.2021.100541

Cited by 77 publications

(52 citation statements)

References 135 publications

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“…As shown in Figure 4a, three distinguishable peaks (P 1 , P 2 , and P 3 ) are observed: P 1 (pulse pressure) is the difference between systolic (P Sys ) and diastolic (P Dia ) pressures due to the blood flow ejected by the heart contraction; P 2 and P 3 are the reflected wave pressures generated by reflected waves from the hand and lower body, respectively. 57,58 More importantly, these resistance signals are fully recoverable and repeatable, verifying the reliability of the PCM sensor.…”

Section: Piezoresistive Performances Of the Pcm Sensorsmentioning

confidence: 64%

Self-Powered Resilient Porous Sensors with Thermoelectric Poly(3,4-ethylenedioxythiophene):Poly(styrenesulfonate) and Carbon Nanotubes for Sensitive Temperature and Pressure Dual-Mode Sensing

Gao

Liu

et al. 2022

ACS Appl. Mater. Interfaces

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Portable and wearable dual-mode sensors that can simultaneously detect multiple stimuli are essential for emerging artificial intelligence applications, and most efforts are devoted to exploring pressure-sensing devices. It is still challenging to integrate temperature and pressure-sensing functions into one sensor without the requirement for complex decoupling processes. Herein, we develop a self-powered and multifunctional dual-mode sensor by dip-coating melamine sponge with both poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and carboxylated single-walled carbon nanotubes (CNTs). By integrating thermoelectric and conductive PEDOT:PSS/CNT components with the hydrophilic and resilient porous sponge, the resultant sensor is efficient in independently detecting temperature and pressure changes. The temperature and pressure stimuli can be independently converted to voltage and electrical resistance signals on the basis of the Seebeck and piezoresistive effects, respectively. The sensor exhibits a high Seebeck coefficient of 35.9 μV K–1 with a minimum temperature detection limit of 0.4 K and a pressure sensitivity of −3.35% kPa–1 with a minimum pressure detection limit of 4 Pa. Interestingly, the sensor can also be self-powered upon illumination. These multi-functionalities make the sensor a promising tool for applications in electronic skin, soft robots, solar energy conversion, and personal health monitoring.

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Section: Piezoresistive Performances Of the Pcm Sensorsmentioning

confidence: 64%

Self-Powered Resilient Porous Sensors with Thermoelectric Poly(3,4-ethylenedioxythiophene):Poly(styrenesulfonate) and Carbon Nanotubes for Sensitive Temperature and Pressure Dual-Mode Sensing

Gao

Liu

et al. 2022

ACS Appl. Mater. Interfaces

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“…An emerging area within electromechanical transducers involves acoustic and ultrasonic sensors. These sensors rely on piezoelectric and composite materials with skin-like mechanical properties to convert subtle acoustic vibrations produced by vasoconstriction and vasodilation events into electrical signals for the continuous monitoring of cardiovascular events 19 , 58 , 117 . Analogue and digital signal processing methods are often applied to the captured waveforms to remove motion artefacts and improve the SNR; multisensor configurations can also be used for active noise-cancelling and localization by beamforming 118 , 119 .…”

Section: Assembling Wearable Devicesmentioning

confidence: 99%

End-to-end design of wearable sensors

et al. 2022

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Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors.

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“…However, such devices are not used as diagnostic tools yet for several reasons, including ethical aspects, limitations in the infrastructure, and concerns related to data protection ( 4 ). Nonetheless, wearable sensors have been used in several applications for research purposes, ranging from rehabilitation ( 4 ) and sport ( 5 ) to cardiovascular monitoring ( 6 ) and emotion recognition ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment and Morphological Analysis of Photoplethysmography in Daily Life

Moscato

Palmerini

Palumbo

et al. 2022

Front. Digit. Health

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The photoplethysmographic (PPG) signal has been applied in various research fields, with promising results for its future clinical application. However, there are several sources of variability that, if not adequately controlled, can hamper its application in pervasive monitoring contexts. This study assessed and characterized the impact of several sources of variability, such as physical activity, age, sex, and health state on PPG signal quality and PPG waveform parameters (Rise Time, Pulse Amplitude, Pulse Time, Reflection Index, Delta T, and DiastolicAmplitude). We analyzed 31 24 h recordings by as many participants (19 healthy subjects and 12 oncological patients) with a wristband wearable device, selecting a set of PPG pulses labeled with three different quality levels. We implemented a Multinomial Logistic Regression (MLR) model to evaluate the impact of the aforementioned factors on PPG signal quality. We then extracted six parameters only on higher-quality PPG pulses and evaluated the influence of physical activity, age, sex, and health state on these parameters with Generalized Linear Mixed Effects Models (GLMM). We found that physical activity has a detrimental effect on PPG signal quality quality (94% of pulses with good quality when the subject is at rest vs. 9% during intense activity), and that health state affects the percentage of available PPG pulses of the best quality (at rest, 44% for healthy subjects vs. 13% for oncological patients). Most of the extracted parameters are influenced by physical activity and health state, while age significantly impacts two parameters related to arterial stiffness. These results can help expand the awareness that accurate, reliable information extracted from PPG signals can be reached by tackling and modeling different sources of inaccuracy.

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Wearable sensors and devices for real-time cardiovascular disease monitoring

Cited by 77 publications

References 135 publications

Self-Powered Resilient Porous Sensors with Thermoelectric Poly(3,4-ethylenedioxythiophene):Poly(styrenesulfonate) and Carbon Nanotubes for Sensitive Temperature and Pressure Dual-Mode Sensing

Self-Powered Resilient Porous Sensors with Thermoelectric Poly(3,4-ethylenedioxythiophene):Poly(styrenesulfonate) and Carbon Nanotubes for Sensitive Temperature and Pressure Dual-Mode Sensing

End-to-end design of wearable sensors

Quality Assessment and Morphological Analysis of Photoplethysmography in Daily Life

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