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13 pages, 2549 KiB

Open AccessArticle

Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices

by Bing Xue, Wen Shi, Sanjay H. Chotirmall, Vivian Ci Ai Koh, Yi Yang Ang, Rex Xiao Tan and Wee Ser

Sensors 2022, 22(6), 2167; https://doi.org/10.3390/s22062167 - 10 Mar 2022

Cited by 5 | Viewed by 2130

Abstract

Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are [...] Read more.

Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm’s low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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10 pages, 1226 KiB

Open AccessArticle

Heart Rate Variability as a Potential Indicator of Cancer Pain in a Mouse Model of Peritoneal Metastasis

by Yurim Kim, Hong Yeol Yoon, Il Keun Kwon, Inchan Youn and Sungmin Han

Sensors 2022, 22(6), 2152; https://doi.org/10.3390/s22062152 - 10 Mar 2022

Cited by 3 | Viewed by 3836

Abstract

Heart rate variability (HRV) is closely related to changes in the autonomic nervous system (ANS) associated with stress and pain. In this study, we investigated whether HRV could be used to assess cancer pain in mice with peritoneal metastases. At 12 days after [...] Read more.

Heart rate variability (HRV) is closely related to changes in the autonomic nervous system (ANS) associated with stress and pain. In this study, we investigated whether HRV could be used to assess cancer pain in mice with peritoneal metastases. At 12 days after cancer induction, positive indicators of pain such as physiological characteristics, appearance, posture, and activity were observed, and time- and frequency-domain HRV parameters such as mean R-R interval, square root of the mean squared differences of successive R-R intervals, and percentage of successive R-R interval differences greater than 5 ms, low frequency (LF), high frequency (HF), and ratio of LF and HF power, were found to be significantly decreased. These parameters returned to normal after analgesic administration. Our results indicate that overall ANS activity was decreased by cancer pain and that HRV could be a useful tool for assessing pain. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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12 pages, 1686 KiB

Open AccessCommunication

Estimating Resting HRV during fMRI: A Comparison between Laboratory and Scanner Environment

by Andy Schumann, Stefanie Suttkus and Karl-Jürgen Bär

Sensors 2021, 21(22), 7663; https://doi.org/10.3390/s21227663 - 18 Nov 2021

Cited by 2 | Viewed by 1924

Abstract

Heart rate variability (HRV) is regularly assessed in neuroimaging studies as an indicator of autonomic, emotional or cognitive processes. In this study, we investigated the influence of a loud and cramped environment during magnetic resonance imaging (MRI) on resting HRV measures. We compared [...] Read more.

Heart rate variability (HRV) is regularly assessed in neuroimaging studies as an indicator of autonomic, emotional or cognitive processes. In this study, we investigated the influence of a loud and cramped environment during magnetic resonance imaging (MRI) on resting HRV measures. We compared recordings during functional MRI sessions with recordings in our autonomic laboratory (LAB) in 101 healthy subjects. In the LAB, we recorded an electrocardiogram (ECG) and a photoplethysmogram (PPG) over 15 min. During resting state functional MRI, we acquired a PPG for 15 min. We assessed anxiety levels before the scanning in each subject. In 27 participants, we performed follow-up sessions to investigate a possible effect of habituation. We found a high intra-class correlation ranging between 0.775 and 0.996, indicating high consistency across conditions. We observed no systematic influence of the MRI environment on any HRV index when PPG signals were analyzed. However, SDNN and RMSSD were significantly higher when extracted from the PPG compared to the ECG. Although we found a significant correlation of anxiety and the decrease in HRV from LAB to MRI, a familiarization session did not change the HRV outcome. Our results suggest that psychological factors are less influential on the HRV outcome during MRI than the methodological choice of the cardiac signal to analyze. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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19 pages, 52245 KiB

Open AccessArticle

SIMpLE: A Mobile Cloud-Based System for Health Monitoring of People with ALS

by Arrigo Palumbo, Nicola Ielpo, Barbara Calabrese, Domenico Corchiola, Remo Garropoli, Vera Gramigna and Giovanni Perri

Sensors 2021, 21(21), 7239; https://doi.org/10.3390/s21217239 - 30 Oct 2021

Cited by 5 | Viewed by 2686

Abstract

Adopting telemonitoring services during the pandemic for people affected by chronic disease is fundamental to ensure access to health care services avoiding the risk of COVID-19 infection. Among chronic diseases, Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s disease, is a progressive [...] Read more.

Adopting telemonitoring services during the pandemic for people affected by chronic disease is fundamental to ensure access to health care services avoiding the risk of COVID-19 infection. Among chronic diseases, Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s disease, is a progressive neurodegenerative disease of adulthood, caused by the loss of spinal, bulbar and cortical motor neurons, which leads to paralysis of the voluntary muscles and, also, involves respiratory ones. Therefore, remote monitoring and teleconsulting are essential services for ALS patients with limited mobility, as the disease progresses, and for those living far from ALS centres and hospitals. In addition, the COVID 19 pandemic has increased the need to remotely provide the best care to patients, avoiding infection during ALS centre visits. The paper illustrates an innovative, secure medical monitoring and teleconsultation mobile cloud-based system for disabled people, such as those with ALS (Amyotrophic Lateral Sclerosis). The design aims to remotely monitor biosignals, such as ECG (electrocardiographic) and EMG (electromyographic) signals of ALS patients in order to prevent complications related to the pathology. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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14 pages, 3186 KiB

Open AccessCommunication

Microneedle Array Electrode-Based Wearable EMG System for Detection of Driver Drowsiness through Steering Wheel Grip

by Afraiz Tariq Satti, Jiyoun Kim, Eunsurk Yi, Hwi-young Cho and Sungbo Cho

Sensors 2021, 21(15), 5091; https://doi.org/10.3390/s21155091 - 27 Jul 2021

Cited by 26 | Viewed by 3554

Abstract

Driver drowsiness is a major cause of fatal accidents throughout the world. Recently, some studies have investigated steering wheel grip force-based alternative methods for detecting driver drowsiness. In this study, a driver drowsiness detection system was developed by investigating the electromyography (EMG) signal [...] Read more.

Driver drowsiness is a major cause of fatal accidents throughout the world. Recently, some studies have investigated steering wheel grip force-based alternative methods for detecting driver drowsiness. In this study, a driver drowsiness detection system was developed by investigating the electromyography (EMG) signal of the muscles involved in steering wheel grip during driving. The EMG signal was measured from the forearm position of the driver during a one-hour interactive driving task. Additionally, the participant’s drowsiness level was also measured to investigate the relationship between muscle activity and driver’s drowsiness level. Frequency domain analysis was performed using the short-time Fourier transform (STFT) and spectrogram to assess the frequency response of the resultant signal. An EMG signal magnitude-based driver drowsiness detection and alertness algorithm is also proposed. The algorithm detects weak muscle activity by detecting the fall in EMG signal magnitude due to an increase in driver drowsiness. The previously presented microneedle electrode (MNE) was used to acquire the EMG signal and compared with the signal obtained using silver-silver chloride (Ag/AgCl) wet electrodes. The results indicated that during the driving task, participants’ drowsiness level increased while the activity of the muscles involved in steering wheel grip decreased concurrently over time. Frequency domain analysis showed that the frequency components shifted from the high to low-frequency spectrum during the one-hour driving task. The proposed algorithm showed good performance for the detection of low muscle activity in real time. MNE showed highly comparable results with dry Ag/AgCl electrodes, which confirm its use for EMG signal monitoring. The overall results indicate that the presented method has good potential to be used as a driver’s drowsiness detection and alertness system. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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13 pages, 3067 KiB

Open AccessArticle

Methods and Experiments for Sensing Variations in Solar Activity and Defining Their Impact on Heart Variability

by Michael Hanzelka, Jiří Dan, Zoltán Szabó, Zdeněk Roubal, Přemysl Dohnal and Radim Kadlec

Sensors 2021, 21(14), 4817; https://doi.org/10.3390/s21144817 - 14 Jul 2021

Cited by 2 | Viewed by 1756

Abstract

This paper evaluates variations in solar activity and their impact on the human nervous system, including the manner in which human behavior and decision-making reflect such effects in the context of (symmetrical) social interactions. The relevant research showed that solar activity, manifesting itself [...] Read more.

This paper evaluates variations in solar activity and their impact on the human nervous system, including the manner in which human behavior and decision-making reflect such effects in the context of (symmetrical) social interactions. The relevant research showed that solar activity, manifesting itself through the exposure of the Earth to charged particles from the Sun, affects heart variability. The evaluation methods focused on examining the relationships between selected psychophysiological data and solar activity, which generally causes major alterations in the low-level electromagnetic field. The investigation within this paper revealed that low-level EMF changes are among the factors affecting heart rate variability and, thus, also variations at the spectral level of the rate, in the VLF, (f = 0.01–0.04 Hz), LF (f = 0.04–0.15 Hz), and HF (f = 0.15 až 0.40 Hz) bands. The results of the presented experiments can also be interpreted as an indirect explanation of sudden deaths and heart failures. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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10 pages, 1017 KiB

Open AccessCommunication

Which Factors Affect the Stress of Intraoperative Orthopedic Surgeons by Using Electroencephalography Signals and Heart Rate Variability?

by Ji-Won Kwon, Soo-Bin Lee, Sahyun Sung, Yung Park, Joong-Won Ha, Gihun Kim, Kyung-Soo Suk, Hak-Sun Kim, Hwan-Mo Lee, Seong-Hwan Moon and Byung Ho Lee

Sensors 2021, 21(12), 4016; https://doi.org/10.3390/s21124016 - 10 Jun 2021

Cited by 11 | Viewed by 2528

Abstract

Can we recognize intraoperative real-time stress of orthopedic surgeons and which factors affect the stress of intraoperative orthopedic surgeons with EEG and HRV? From June 2018 to November 2018, 265 consecutive records of intraoperative stress measures for orthopedic surgeons were compared. Intraoperative EEG [...] Read more.

Can we recognize intraoperative real-time stress of orthopedic surgeons and which factors affect the stress of intraoperative orthopedic surgeons with EEG and HRV? From June 2018 to November 2018, 265 consecutive records of intraoperative stress measures for orthopedic surgeons were compared. Intraoperative EEG waves and HRV, comprising beats per minute (BPM) and low frequency (LF)/high frequency (HF) ratio were gathered for stress-associated parameters. Differences in stress parameters according to the experience of surgeons, intraoperative blood loss, and operation time depending on whether or not a tourniquet were investigated. Stress-associated EEG signals including beta 3 waves were significantly higher compared to EEG at rest for novice surgeons as the procedure progressed. Among senior surgeons, the LF/HF ratio reflecting the physical demands of stress was higher than that of novice surgeons at all stages. In surgeries including tourniquets, operation time was positively correlated with stress parameters including beta 1, beta 2, beta 3 waves and BPM. In non-tourniquet orthopedic surgeries, intraoperative blood loss was positively correlated with beta 1, beta 2, and beta 3 waves. Among orthopedic surgeons, those with less experience demonstrated relatively higher levels of stress during surgery. Prolonged operation time or excessive intraoperative blood loss appear to be contributing factors that increase stress. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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16 pages, 13853 KiB

Open AccessArticle

Radar-Based, Simultaneous Human Presence Detection and Breathing Rate Estimation

by Nir Regev and Dov Wulich

Sensors 2021, 21(10), 3529; https://doi.org/10.3390/s21103529 - 19 May 2021

Cited by 5 | Viewed by 3900

Abstract

Human presence detection is an application that has a growing need in many industries. Hotel room occupancy is critical for electricity and energy conservation. Industrial factories and plants have the same need to know the occupancy status to regulate electricity, lighting, and energy [...] Read more.

Human presence detection is an application that has a growing need in many industries. Hotel room occupancy is critical for electricity and energy conservation. Industrial factories and plants have the same need to know the occupancy status to regulate electricity, lighting, and energy expenditures. In home security there is an obvious necessity to detect human presence inside the residence. For elderly care and healthcare, the system would like to know if the person is sleeping in the room, sitting on a sofa or conversely, is not present. This paper focuses on the problem of detecting presence using only the minute movements of breathing while at the same time estimating the breathing rate, which is the secondary aim of the paper. We extract the suspected breathing signal, and construct its Fourier series (FS) equivalent. Then we employ a generalized likelihood ratio test (GLRT) on the FS signal to determine if it is a breathing pattern or noise. We will show that calculating the GLRT also yields the maximum likelihood (ML) estimator for the breathing rate. We tested this algorithm on sleeping babies as well as conducted experiments on humans aged 12 to 44 sitting on a chair in front of the radar. The results are reported in the sequel. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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13 pages, 1058 KiB

Open AccessArticle

The Effects of Inter-Set Recovery Time on Explosive Power, Electromyography Activity, and Tissue Oxygenation during Plyometric Training

by Shuo Guan, Nan Lin, Yue Yin, Haibin Liu, Liqing Liu and Liping Qi

Sensors 2021, 21(9), 3015; https://doi.org/10.3390/s21093015 - 25 Apr 2021

Cited by 10 | Viewed by 6090

Abstract

Performing continuous sets to failure is fatiguing during the plyometric training. Cluster sets have been used to redistribute total rest time to create short frequent sets so that muscle fatigue can be avoided. The purpose of the study was to investigate the effects [...] Read more.

Performing continuous sets to failure is fatiguing during the plyometric training. Cluster sets have been used to redistribute total rest time to create short frequent sets so that muscle fatigue can be avoided. The purpose of the study was to investigate the effects of inter-set recovery time on lower extremity explosive power, neuromuscular activity, and tissue oxygenation during plyometric exercise and recovery. An integrated assessment of explosive power, muscle electrical activity, and tissue oxygenation was adopted in the present study to help understand local muscle metabolism and fatigue during plyometric exercise and recovery. Ten university male basketball players participated in this study. Subjects performed 4 groups of exercise, each group comprised of 3 sets of jumps: 1, 2, 3, or 5 min. Surface electromyography (sEMG) signals were collected from 9 lower extremity muscles; near-infrared spectroscopy (NIRS) was recorded on vastus lateralis; mechanical data during plyometric exercise were collected from a force plate. No significant differences among sets and among groups were found regarding explosive power, jump height, EMG intensity, mean power frequency, the rate of tissue saturation index, and HbO₂ changes between baseline and recovery. The current study has shown no muscular fatigue induced during the 4 groups of exercise. The results of this study may help inform recommendations concerning the recovery time during plyometric exercises at low loads (30% 1 RM). Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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29 pages, 10124 KiB

Open AccessArticle

Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial Regularization

by Juan Lorenzo Hagad, Tsukasa Kimura, Ken-ichi Fukui and Masayuki Numao

Sensors 2021, 21(5), 1792; https://doi.org/10.3390/s21051792 - 04 Mar 2021

Cited by 9 | Viewed by 3504

Abstract

Two of the biggest challenges in building models for detecting emotions from electroencephalography (EEG) devices are the relatively small amount of labeled samples and the strong variability of signal feature distributions between different subjects. In this study, we propose a context-generalized model that [...] Read more.

Two of the biggest challenges in building models for detecting emotions from electroencephalography (EEG) devices are the relatively small amount of labeled samples and the strong variability of signal feature distributions between different subjects. In this study, we propose a context-generalized model that tackles the data constraints and subject variability simultaneously using a deep neural network architecture optimized for normally distributed subject-independent feature embeddings. Variational autoencoders (VAEs) at the input level allow the lower feature layers of the model to be trained on both labeled and unlabeled samples, maximizing the use of the limited data resources. Meanwhile, variational regularization encourages the model to learn Gaussian-distributed feature embeddings, resulting in robustness to small dataset imbalances. Subject-adversarial regularization applied to the bi-lateral features further enforces subject-independence on the final feature embedding used for emotion classification. The results from subject-independent performance experiments on the SEED and DEAP EEG-emotion datasets show that our model generalizes better across subjects than other state-of-the-art feature embeddings when paired with deep learning classifiers. Furthermore, qualitative analysis of the embedding space reveals that our proposed subject-invariant bi-lateral variational domain adversarial neural network (BiVDANN) architecture may improve the subject-independent performance by discovering normally distributed features. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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11 pages, 1355 KiB

Open AccessCommunication

Cortical Effects of Noisy Galvanic Vestibular Stimulation Using Functional Near-Infrared Spectroscopy

by Bulmaro A. Valdés, Kim Lajoie, Daniel S. Marigold and Carlo Menon

Sensors 2021, 21(4), 1476; https://doi.org/10.3390/s21041476 - 20 Feb 2021

Cited by 4 | Viewed by 3131

Abstract

Noisy galvanic vestibular stimulation (nGVS) can improve different motor, sensory, and cognitive behaviors. However, it is unclear how this stimulation affects brain activity to facilitate these improvements. Functional near-infrared spectroscopy (fNIRS) is inexpensive, portable, and less prone to motion artifacts than other neuroimaging [...] Read more.

Noisy galvanic vestibular stimulation (nGVS) can improve different motor, sensory, and cognitive behaviors. However, it is unclear how this stimulation affects brain activity to facilitate these improvements. Functional near-infrared spectroscopy (fNIRS) is inexpensive, portable, and less prone to motion artifacts than other neuroimaging technology. Thus, fNIRS has the potential to provide insight into how nGVS affects cortical activity during a variety of natural behaviors. Here we sought to: (1) determine if fNIRS can detect cortical changes in oxygenated (HbO) and deoxygenated (HbR) hemoglobin with application of subthreshold nGVS, and (2) determine how subthreshold nGVS affects this fNIRS-derived hemodynamic response. A total of twelve healthy participants received nGVS and sham stimulation during a seated, resting-state paradigm. To determine whether nGVS altered activity in select cortical regions of interest (BA40, BA39), we compared differences between nGVS and sham HbO and HbR concentrations. We found a greater HbR response during nGVS compared to sham stimulation in left BA40, a region previously associated with vestibular processing, and with all left hemisphere channels combined (p < 0.05). We did not detect differences in HbO responses for any region during nGVS (p > 0.05). Our results suggest that fNIRS may be suitable for understanding the cortical effects of nGVS. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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13 pages, 2860 KiB

Open AccessArticle

Novel Phonography-Based Measurement for Fetal Breathing Movement in the Third Trimester

by Márton Áron Goda, Tamás Telek and Ferenc Kovács

Sensors 2021, 21(1), 211; https://doi.org/10.3390/s21010211 - 31 Dec 2020

Cited by 4 | Viewed by 2855

Abstract

The detailed assessment of fetal breathing movement (FBM) monitoring can be a pre-indicator of many critical cases in the third trimester of pregnancy. Standard 3D ultrasound monitoring is time-consuming for FBM detection. Therefore, this type of measurement is not common. The main goal [...] Read more.

The detailed assessment of fetal breathing movement (FBM) monitoring can be a pre-indicator of many critical cases in the third trimester of pregnancy. Standard 3D ultrasound monitoring is time-consuming for FBM detection. Therefore, this type of measurement is not common. The main goal of this research is to provide a comprehensive image about FBMs, which can also have potential for application in telemedicine. Fifty pregnancies were examined by phonography, and nearly 9000 FBMs were identified. In the case of male and female fetuses, 4740 and 3100 FBM episodes were detected, respectively. The measurements proved that FBMs are well detectable in the 20–30 Hz frequency band. For these episodes, an average duration of 1.008 ± 0.13 s (p < 0.03) was measured in the third trimester. The recorded material lasted for 16 h altogether. Based on these measurements, an accurate assessment of FBMs could be performed. The epochs can be divided into smaller-episode groups separated by shorter breaks. During the pregnancy, the rate of these breaks continuously decreases, and episode groups become more contiguous. However, there are significant differences between male and female fetuses. The proportion of the episodes which were classified into minimally 10-member episode groups was 19.7% for males and only 12.1% for females, even at the end of the third trimester. In terms of FBM detection, phonography offers a novel opportunity for long-term monitoring. Combined with cardiac diagnostic methods, it can be used for fetal activity assessment in the third trimester and make measurement appreciably easier than before. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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19 pages, 1810 KiB

Open AccessArticle

Vital Sign Monitoring Using FMCW Radar in Various Sleeping Scenarios

by Emmi Turppa, Juha M. Kortelainen, Oleg Antropov and Tero Kiuru

Sensors 2020, 20(22), 6505; https://doi.org/10.3390/s20226505 - 14 Nov 2020

Cited by 52 | Viewed by 7218

Abstract

Remote monitoring of vital signs for studying sleep is a user-friendly alternative to monitoring with sensors attached to the skin. For instance, remote monitoring can allow unconstrained movement during sleep, whereas detectors requiring a physical contact may detach and interrupt the measurement and [...] Read more.

Remote monitoring of vital signs for studying sleep is a user-friendly alternative to monitoring with sensors attached to the skin. For instance, remote monitoring can allow unconstrained movement during sleep, whereas detectors requiring a physical contact may detach and interrupt the measurement and affect sleep itself. This study evaluates the performance of a cost-effective frequency modulated continuous wave (FMCW) radar in remote monitoring of heart rate and respiration in scenarios resembling a set of normal and abnormal physiological conditions during sleep. We evaluate the vital signs of ten subjects in different lying positions during various tasks. Specifically, we aim for a broad range of both heart and respiration rates to replicate various real-life scenarios and to test the robustness of the selected vital sign extraction methods consisting of fast Fourier transform based cepstral and autocorrelation analyses. As compared to the reference signals obtained using Embla titanium, a certified medical device, we achieved an overall relative mean absolute error of

3.6

% (86% correlation) and

9.1

% (91% correlation) for the heart rate and respiration rate, respectively. Our results promote radar-based clinical monitoring by showing that the proposed radar technology and signal processing methods accurately capture even such alarming vital signs as minimal respiration. Furthermore, we show that common parameters for heart rate variability can also be accurately extracted from the radar signal, enabling further sleep analyses. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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21 pages, 957 KiB

Open AccessSystematic Review

Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review

by Martin Clinton Tosima Manullang, Yuan-Hsiang Lin, Sheng-Jie Lai and Nai-Kuan Chou

Sensors 2021, 21(23), 7777; https://doi.org/10.3390/s21237777 - 23 Nov 2021

Cited by 17 | Viewed by 6093

Abstract

Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to [...] Read more.

Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future. Full article

(This article belongs to the Special Issue Neurophysiological Monitoring)

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