Contactless Technologies, Sensors, and Systems for Cardiac and Respiratory Measurement during Sleep: A Systematic Review

Sensors, Год журнала: 2023, Номер 23(11), С. 5038 - 5038

Опубликована: Май 24, 2023

Sleep is essential to physical and mental health. However, the traditional approach sleep analysis—polysomnography (PSG)—is intrusive expensive. Therefore, there great interest in development of non-contact, non-invasive, non-intrusive monitoring systems technologies that can reliably accurately measure cardiorespiratory parameters with minimal impact on patient. This has led other relevant approaches, which are characterised, for example, by fact they allow greater freedom movement do not require direct contact body, i.e., non-contact. systematic review discusses methods non-contact activity during sleep. Taking into account current state art technologies, we identify cardiac respiratory activity, types sensors used, possible physiological available analysis. To this, conducted a literature summarised research use activity. The inclusion exclusion criteria selection publications were established prior start search. Publications assessed using one main question several specific questions. We obtained 3774 unique articles from four databases (Web Science, IEEE Xplore, PubMed, Scopus) checked them relevance, resulting 54 analysed structured way terminology. result was 15 different devices (e.g., radar, temperature sensors, motion cameras) be installed hospital wards departments or environment. ability detect heart rate, disorders such as apnoea among characteristics examined investigate overall effectiveness considered monitoring. In addition, advantages disadvantages identified answering results us determine trends vector medical medicine future researchers research.

Язык: Английский

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Fast-Response Non-Contact Flexible Humidity Sensor Based on Direct-Writing Printing for Respiration Monitoring

Biosensors, Год журнала: 2023, Номер 13(8), С. 792 - 792

Опубликована: Авг. 7, 2023

Respiratory monitoring is crucial for evaluating health status and identifying potential respiratory diseases such as failure, bronchitis, pneumonia. Humidity sensors play a significant role in this regard, efforts are being made to improve their performance. However, achieving ideal sensor parameters sensitivity, detection range, response speed challenging. In work, we propose flexible preparation method double-layer humidity using PDMS substrate GNP/MWCNT composite material element. This exhibits high sensitivity (1.4 RH-1), wide range (20-90%), ultra-fast (0.35 s) recovery (2.5 s), repetitiveness (500 cycles), good long-term stability, excellent flexibility. Due these advantages, has applications real-time clinical home medical care, accurate human non-invasive skin monitoring. Hence, can be powerful tool monitor moisture levels diagnosing treating effectively.

Язык: Английский

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In-Home, Smart Sleep Monitoring System for Cardiorespiratory Estimation and Sleep Apnea Detection: Proof of Concept

IEEE Sensors Journal, Год журнала: 2024, Номер 24(8), С. 13364 - 13377

Опубликована: Март 4, 2024

Apnea is a sleep disorder characterized by breathing interruptions during sleep, impacting cardiorespiratory function and overall health. Traditional diagnostic methods, like polysomnography (PSG), are unobtrusive, leading to noninvasive monitoring. This study aims develop validate novel monitoring system using sensor technology estimate parameters detect apnea. We designed seamless integrating noncontact force-sensitive resistor sensors collect ballistocardiogram signals associated with activity. enhanced the sensor's sensitivity reduced noise designing new concept of edge-measuring hemisphere dome mechanical hanger distribute force mechanically amplify micromovement caused cardiac respiration activities. In total, we deployed three sensors, two under thoracic one abdominal regions. The supported onboard signal preprocessing in multiple physical layers mattress. collected data four sleeping positions from 16 subjects analyzed them ensemble empirical mode decomposition (EMD) avoid frequency mixing. also developed an adaptive thresholding method identify error was 3.98 1.43 beats/min (BPM) heart rate (HR) estimation, respectively. apnea detected accuracy 87%. optimized such that only can measure parameters. Such reduction complexity simplification instruction use shows excellent potential for in-home continuous

Язык: Английский

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Remote monitoring of sleep disorder using FBG sensors and FSO transmission system enabled smart vest

Engineering Research Express, Год журнала: 2024, Номер 6(2), С. 025337 - 025337

Опубликована: Май 8, 2024

Abstract Optical sensors, particularly fiber Bragg grating (FBG) sensors have achieved a fast ingress into the fields of medical diagnostic and vital signs monitoring. Wearable smart textiles equipped with FBG are catching huge research attention in different applications for measurement monitoring physiological parameters. In this paper, we report simple technique remote sleep disorder using vest implemented four stress located at sides free space optics (FSO) transmission system. The patient is monitored real time through shifts original wavelengths by loading during random changes patient’s sleeping postures. reflected wavelength from loaded sensor certain posture transmitted over 0.5 km long FSO channel towards center, photodetected, then can be processed PC to record restlessness interval terms total number times postures changed, spent etc. To correctly detect various parameters demultiplexer carefully adjusted minimize power leakages unloaded that may result errors detection. Maximum dynamic range around 45 dB has been ensuring accurate This study not only provides cost-efficient non-intrusive solution patients but also used real-time other ailments, such as lung, brain, cardiac diseases future.

Язык: Английский

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Monitoring of Cardiorespiratory Parameters during Sleep Using a Special Holder for the Accelerometer Sensor

Sensors, Год журнала: 2023, Номер 23(11), С. 5351 - 5351

Опубликована: Июнь 5, 2023

Sleep is extremely important for physical and mental health. Although polysomnography an established approach in sleep analysis, it quite intrusive expensive. Consequently, developing a non-invasive non-intrusive home monitoring system with minimal influence on patients, that can reliably accurately measure cardiorespiratory parameters, of great interest. The aim this study to validate unobtrusive parameter based accelerometer sensor. This includes special holder install the under bed mattress. additional determine optimum relative position (in relation subject) at which most accurate precise values measured parameters could be achieved. data were collected from 23 subjects (13 males 10 females). obtained ballistocardiogram signal was sequentially processed using sixth-order Butterworth bandpass filter moving average filter. As result, error (compared reference values) 2.24 beats per minute heart rate 1.52 breaths respiratory achieved, regardless subject’s position. For females, errors 2.28 bpm 2.19 1.41 rpm 1.30 rate. We determined placing sensor chest level preferred configuration measurement. Further studies system’s performance larger groups are required, despite promising results current tests healthy subjects.

Язык: Английский

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Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed

Sensors, Год журнала: 2024, Номер 24(6), С. 1900 - 1900

Опубликована: Март 15, 2024

This work aims to compare the performance of Machine Learning (ML) and Deep (DL) algorithms in detecting users’ heartbeats on a smart bed. Targeting non-intrusive, continuous heart monitoring during sleep time, bed is equipped with 3D solid-state accelerometer. Acceleration signals are processed through an STM 32-bit microcontroller board transmitted PC for recording. A photoplethysmographic sensor simultaneously checked ground truth reference. dataset has been built, by acquiring measures real-world set-up: 10 participants were involved, resulting 120 min acceleration traces which utilized train evaluate various Artificial Intelligence (AI) algorithms. The experimental analysis utilizes K-fold cross-validation ensure robust model testing across different subsets dataset. Various ML DL compared, each being trained tested using collected data. Random Forest algorithm exhibited highest accuracy among all compared models. While it requires longer training time some models such as Naïve Bayes, Linear Discrimination Analysis, K-Nearest Neighbour Classification, keeps substantially faster than Support Vector demonstrated metrics, including recall, precision, F1-scores, macro average, weighted overall well above 90%. study highlights better specific use case, achieving superior metrics user comparison other tested. drawback times not too relevant long-term target scenario, so stands out viable solution real-time ballistocardiographic heartbeat detection, showcasing potential healthcare wellness applications.

Язык: Английский

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Neuromorphic Sensor Based on FSR

Опубликована: Апрель 18, 2024

This work introduces a neuromorphic sensor based on force-sensing resistors (FSR) and spiking neurons for robotic systems. The proposed integrates the FSR in schematic of neuron order to make generate spikes with frequency that depends applied force. performance is evaluated control SMA-actuated ro-botic finger by monitoring force during steady state when pushes tweezers. For comparison purposes, we performed similar evaluation SNN receives input from widely used compression load cell (CLC). results show neuro-morphic has very good sensitivity low forces function between rate continuous variation range. However, compared CLC, lower linearity response, but it benefits two orders magnitude power consumption. These aspects imply are useful bioinspired humanoid robotics representing cost alternative sensors.

Язык: Английский

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Neuromorphic Sensor Based on Force-Sensing Resistors

Biomimetics, Год журнала: 2024, Номер 9(6), С. 326 - 326

Опубликована: Май 29, 2024

This work introduces a neuromorphic sensor (NS) based on force-sensing resistors (FSR) and spiking neurons for robotic systems. The proposed integrates the FSR in schematic of neuron order to make generate spikes with frequency that depends applied force. performance is evaluated control SMA-actuated finger by monitoring force during steady state when pushes tweezer. For comparison purposes, we performed similar evaluation SNN received input from widely used compression load cell (CLC). results show FSR-based has very good sensitivity low forces function between rate continuous, variation range. However, compared CLC, response NS follows logarithmic-like improved small forces. In addition, power consumption 128 µW 270 times lower than CLC which needs 3.5 mW operate. These characteristics suitable bioinspired humanoid robotics, representing low-power low-cost alternative sensors.

Язык: Английский

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Performance improvement of cardiorespiratory measurements using pressure sensors with mechanical coupling techniques

Procedia Computer Science, Год журнала: 2023, Номер 225, С. 1891 - 1899

Опубликована: Янв. 1, 2023

Monitoring heart rate and breathing is essential in understanding the physiological processes for sleep analysis. Polysomnography (PSG) system have traditionally been used monitoring, but alternative methods can help to make monitoring more portable someone's home. This study conducted a series of experiments investigate use pressure sensors placed under bed as an PSG during sleep. The following sets involved addition small rubber domes - transparent black that were glued sensor. resulting data compared with determine accuracy sensor readings. found provided reliable extracting respiration rate, mean absolute errors (MAE) 2.32 3.24 respectively. However, hemispheres did not significantly improve readings, MAEs 2.3 bpm 7.56 breaths per minute findings this suggest may serve viable traditional systems These provide comfortable non-invasive method monitoring. enhance indicating it be worthwhile system.

Язык: Английский

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Non-invasive System for Sleep Assessment: Software Components and Information Flow

Procedia Computer Science, Год журнала: 2024, Номер 246, С. 5378 - 5387

Опубликована: Янв. 1, 2024

Язык: Английский

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