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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 79053

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Special Issue Editors

Dr. Wenjin Wang

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Guest Editor

Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
Interests: biomedical engineering; signal and image processing; computer vision; cameras; healthcare

Prof. Steffen Leonhardt

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Philips Chair for Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, D-52074 Aachen, Germany
Interests: physiological measurement techniques; personal health care systems and feedback control systems in medicine
Special Issues, Collections and Topics in MDPI journals

Prof. Daqing Zhang

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Guest Editor

Telecom SudParis/Peking University
Interests: pervasive computing; context awareness; mobile sensing; urban computing; smart environments

Dr. Bert den Brinker

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Guest Editor

Department of Data Science, Philips Research, 5656 AE Eindhoven, The Netherlands
Interests: biomedical engineering; signal processing; audio; healthcare

Special Issue Information

Dear Colleagues,

Monitoring based on contactless sensing is an emerging research topic with numerous biomedical and healthcare applications. For instance, it could potentially help to control pandemics like COVID-19.

Various contactless sensors, such as remote optical (RGB, Infrared, Terahertz cameras), radio frequency (radar, WiFi), acoustic, capacitive, and magnetic sensors, can be exploited to measure physiological signals (e.g., heart rate, respiration rate, blood oxygen saturation, blood pressure, skin temperature) and activity signals (e.g., movement, emotion, context) from a human body to assess health conditions. The fusion of different sensor streams is a source of new insights into health informatics. Further development of these technologies will lead to a rich set of compelling health applications to improve quality of life, care experience, and clinical workflow, ranging from hospital care units to ordinary homes.

This Special Issue will highlight, but is not limited to, the following directions:

Innovative or improved sensors and sensor fusion for contactless health monitoring;
Novel approaches (methodologies or algorithms) for processing the sensory information (e.g., signal/image processing, data analysis, AI);
New applications and studies for contactless health monitoring (e.g., clinical trials, use cases in combating COVID-19).

We look forward to receiving your high-quality and original research work and welcome your participation in this Special Issue.

Dr. Wenjin Wang
Prof. Steffen Leonhardt
Prof. Daqing Zhang
Dr. Bert den Brinker
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Contactless sensors
health monitoring
healthcare
camera
radio frequency
wireless
biomedical sensing
physiological measurement
COVID-19

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Published Papers (13 papers)

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14 pages, 3070 KiB

Open AccessCommunication

Multitask Siamese Network for Remote Photoplethysmography and Respiration Estimation

by Heejin Lee, Junghwan Lee, Yujin Kwon, Jiyoon Kwon, Sungmin Park, Ryanghee Sohn and Cheolsoo Park

Sensors 2022, 22(14), 5101; https://doi.org/10.3390/s22145101 - 7 Jul 2022

Cited by 7 | Viewed by 2563

Abstract

Heart and respiration rates represent important vital signs for the assessment of a person’s health condition. To estimate these vital signs accurately, we propose a multitask Siamese network model (MTS) that combines the advantages of the Siamese network and the multitask learning architecture. The MTS model was trained by the images of the cheek including nose and mouth and forehead areas while sharing the same parameters between the Siamese networks, in order to extract the features about the heart and respiratory information. The proposed model was constructed with a small number of parameters and was able to yield a high vital-sign-prediction accuracy, comparable to that obtained from the single-task learning model; furthermore, the proposed model outperformed the conventional multitask learning model. As a result, we can simultaneously predict the heart and respiratory signals with the MTS model, while the number of parameters was reduced by 16 times with the mean average errors of heart and respiration rates being 2.84 and 4.21. Owing to its light weight, it would be advantageous to implement the vital-sign-monitoring model in an edge device such as a mobile phone or small-sized portable devices. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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18 pages, 3343 KiB

Open AccessArticle

Segmentation of Plantar Foot Thermal Images Using Prior Information

by Asma Bougrine, Rachid Harba, Raphael Canals, Roger Ledee, Meryem Jabloun and Alain Villeneuve

Sensors 2022, 22(10), 3835; https://doi.org/10.3390/s22103835 - 18 May 2022

Cited by 6 | Viewed by 2580

Abstract

Diabetic foot (DF) complications are associated with temperature variations. The occurrence of DF ulceration could be reduced by using a contactless thermal camera. The aim of our study is to provide a decision support tool for the prevention of DF ulcers. Thus, the segmentation of the plantar foot in thermal images is a challenging step for a non-constraining acquisition protocol. This paper presents a new segmentation method for plantar foot thermal images. This method is designed to include five pieces of prior information regarding the aforementioned images. First, a new energy term is added to the snake of Kass et al. in order to force its curvature to match that of the prior shape, which has a known form. Second, we defined the initial contour as the downsized prior-shape contour, which is placed inside the plantar foot surface in a vertical orientation. This choice makes the snake avoid strong false boundaries present outside the plantar region when evolving. As a result, the snake produces a smooth contour that rapidly converges to the true boundaries of the foot. The proposed method is compared to two classical prior-shape snake methods, that of Ahmed et al. and that of Chen et al. A database of 50 plantar foot thermal images was processed. The results show that the proposed method outperforms the previous two methods with a root-mean-square error of 5.12 pixels and a dice similarity coefficient of 94%. The segmentation of the plantar foot regions in the thermal images helped us to assess the point-to-point temperature differences between the two feet in order to detect hyperthermia regions. The presence of such regions is the pre-sign of ulcers in the diabetic foot. Furthermore, our method was applied to hyperthermia detection to illustrate the promising potential of thermography in the case of the diabetic foot. Associated with a friendly acquisition protocol, the proposed segmentation method is the first step for a future mobile smartphone-based plantar foot thermal analysis for diabetic foot patients. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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16 pages, 2309 KiB

Open AccessArticle

Monitoring Respiratory Motion during VMAT Treatment Delivery Using Ultra-Wideband Radar

by Anwar Fallatah, Miodrag Bolic, Miller MacPherson and Daniel J. La Russa

Sensors 2022, 22(6), 2287; https://doi.org/10.3390/s22062287 - 16 Mar 2022

Cited by 7 | Viewed by 2770

Abstract

The goal of this paper is to evaluate the potential of a low-cost, ultra-wideband radar system for detecting and monitoring respiratory motion during radiation therapy treatment delivery. Radar signals from breathing motion patterns simulated using a respiratory motion phantom were captured during volumetric modulated arc therapy (VMAT) delivery. Gantry motion causes strong interference affecting the quality of the extracted respiration motion signal. We developed an artificial neural network (ANN) model for recovering the breathing motion patterns. Next, automated classification into four classes of breathing amplitudes is performed, including no breathing, breath hold, free breathing and deep inspiration. Breathing motion patterns extracted from the radar signal are in excellent agreement with the reference data recorded by the respiratory motion phantom. The classification accuracy of simulated deep inspiration breath hold breathing was 94% under the worst case interference from gantry motion and linac operation. Ultra-wideband radar systems can achieve accurate breathing rate estimation in real-time during dynamic radiation delivery. This technology serves as a viable alternative to motion detection and respiratory gating systems based on surface detection, and is well-suited to dynamic radiation treatment techniques. Novelties of this work include detection of the breathing signal using radar during strong interference from simultaneous gantry motion, and using ANN to perform adaptive signal processing to recover breathing signal from large interference signals in real time. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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25 pages, 7020 KiB

Open AccessArticle

Infrared Thermography for Measuring Elevated Body Temperature: Clinical Accuracy, Calibration, and Evaluation

by Quanzeng Wang, Yangling Zhou, Pejman Ghassemi, David McBride, Jon P. Casamento and T. Joshua Pfefer

Sensors 2022, 22(1), 215; https://doi.org/10.3390/s22010215 - 29 Dec 2021

Cited by 23 | Viewed by 7575

Abstract

Infrared thermographs (IRTs) implemented according to standardized best practices have shown strong potential for detecting elevated body temperatures (EBT), which may be useful in clinical settings and during infectious disease epidemics. However, optimal IRT calibration methods have not been established and the clinical performance of these devices relative to the more common non-contact infrared thermometers (NCITs) remains unclear. In addition to confirming the findings of our preliminary analysis of clinical study results, the primary intent of this study was to compare methods for IRT calibration and identify best practices for assessing the performance of IRTs intended to detect EBT. A key secondary aim was to compare IRT clinical accuracy to that of NCITs. We performed a clinical thermographic imaging study of more than 1000 subjects, acquiring temperature data from several facial locations that, along with reference oral temperatures, were used to calibrate two IRT systems based on seven different regression methods. Oral temperatures imputed from facial data were used to evaluate IRT clinical accuracy based on metrics such as clinical bias (

Δ_{c b}

), repeatability, root-mean-square difference, and sensitivity/specificity. We proposed several calibration approaches designed to account for the non-uniform data density across the temperature range and a constant offset approach tended to show better ability to detect EBT. As in our prior study, inner canthi or full-face maximum temperatures provided the highest clinical accuracy. With an optimal calibration approach, these methods achieved a

Δ_{c b}

between ±0.03 °C with standard deviation (

σ_{Δ c b}

) less than 0.3 °C, and sensitivity/specificity between 84% and 94%. Results of forehead-center measurements with NCITs or IRTs indicated reduced performance. An analysis of the complete clinical data set confirms the essential findings of our preliminary evaluation, with minor differences. Our findings provide novel insights into methods and metrics for the clinical accuracy assessment of IRTs. Furthermore, our results indicate that calibration approaches providing the highest clinical accuracy in the 37–38.5 °C range may be most effective for measuring EBT. While device performance depends on many factors, IRTs can provide superior performance to NCITs. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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16 pages, 3687 KiB

Open AccessArticle

Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection

by Ilde Lorato, Sander Stuijk, Mohammed Meftah, Deedee Kommers, Peter Andriessen, Carola van Pul and Gerard de Haan

Sensors 2021, 21(18), 6306; https://doi.org/10.3390/s21186306 - 21 Sep 2021

Cited by 12 | Viewed by 2920

Abstract

Both Respiratory Flow (RF) and Respiratory Motion (RM) are visible in thermal recordings of infants. Monitoring these two signals usually requires landmark detection for the selection of a region of interest. Other approaches combine respiratory signals coming from both RF and RM, obtaining a Mixed Respiratory (MR) signal. The detection and classification of apneas, particularly common in preterm infants with low birth weight, would benefit from monitoring both RF and RM, or MR, signals. Therefore, we propose in this work an automatic RF pixel detector not based on facial/body landmarks. The method is based on the property of RF pixels in thermal videos, which are in areas with a smooth circular gradient. We defined 5 features combined with the use of a bank of Gabor filters that together allow selection of the RF pixels. The algorithm was tested on thermal recordings of 9 infants amounting to a total of 132 min acquired in a neonatal ward. On average the percentage of correctly identified RF pixels was

84 %

. Obstructive Apneas (OAs) were simulated as a proof of concept to prove the advantage in monitoring the RF signal compared to the MR signal. The sensitivity in the simulated OA detection improved for the RF signal reaching

73 %

against the

23 %

of the MR signal. Overall, the method yielded promising results, although the positioning and number of cameras used could be further optimized for optimal RF visibility. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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18 pages, 3826 KiB

Open AccessArticle

Contactless Gait Assessment in Home-like Environments

by Angela Botros, Nathan Gyger, Narayan Schütz, Michael Single, Tobias Nef and Stephan M. Gerber

Sensors 2021, 21(18), 6205; https://doi.org/10.3390/s21186205 - 16 Sep 2021

Cited by 5 | Viewed by 4080

Abstract

Gait analysis is an important part of assessments for a variety of health conditions, specifically neurodegenerative diseases. Currently, most methods for gait assessment are based on manual scoring of certain tasks or restrictive technologies. We present an unobtrusive sensor system based on light detection and ranging sensor technology for use in home-like environments. In our evaluation, we compared six different gait parameters, based on recordings from 25 different people performing eight different walks each, resulting in 200 unique measurements. We compared the proposed sensor system against two state-of-the art technologies, a pressure mat and a set of inertial measurement unit sensors. In addition to test usability and long-term measurement, multi-hour recordings were conducted. Our evaluation showed very high correlation (

r > 0.95

) with the gold standards across all assessed gait parameters except for cycle time (

r = 0.91

). Similarly, the coefficient of determination was high (

R^{2} > 0.9

) for all gait parameters except cycle time. The highest correlation was achieved for stride length and velocity (

r \geq 0.98, R^{2} \geq 0.95

). Furthermore, the multi-hour recordings did not show the systematic drift of measurements over time. Overall, the unobtrusive gait measurement system allows for contactless, highly accurate long- and short-term assessments of gait in home-like environments. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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27 pages, 2460 KiB

Open AccessArticle

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

by Fabian Schrumpf, Patrick Frenzel, Christoph Aust, Georg Osterhoff and Mirco Fuchs

Sensors 2021, 21(18), 6022; https://doi.org/10.3390/s21186022 - 8 Sep 2021

Cited by 78 | Viewed by 15359

Abstract

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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18 pages, 4119 KiB

Open AccessEditor’s ChoiceArticle

Towards Continuous Camera-Based Respiration Monitoring in Infants

by Ilde Lorato, Sander Stuijk, Mohammed Meftah, Deedee Kommers, Peter Andriessen, Carola van Pul and Gerard de Haan

Sensors 2021, 21(7), 2268; https://doi.org/10.3390/s21072268 - 24 Mar 2021

Cited by 27 | Viewed by 3779

Abstract

Aiming at continuous unobtrusive respiration monitoring, motion robustness is paramount. However, some types of motion can completely hide the respiration information and the detection of these events is required to avoid incorrect rate estimations. Therefore, this work proposes a motion detector optimized to specifically detect severe motion of infants combined with a respiration rate detection strategy based on automatic pixels selection, which proved to be robust to motion of the infants involving head and limbs. A dataset including both thermal and RGB (Red Green Blue) videos was used amounting to a total of 43 h acquired on 17 infants. The method was successfully applied to both RGB and thermal videos and compared to the chest impedance signal. The Mean Absolute Error (MAE) in segments where some motion is present was

1.16

and

1.97

breaths/min higher than the MAE in the ideal moments where the infants were still for testing and validation set, respectively. Overall, the average MAE on the testing and validation set are

3.31

breaths/min and

5.36

breaths/min, using

64.00 %

and

69.65 %

of the included video segments (segments containing events such as interventions were excluded based on a manual annotation), respectively. Moreover, we highlight challenges that need to be overcome for continuous camera-based respiration monitoring. The method can be applied to different camera modalities, does not require skin visibility, and is robust to some motion of the infants. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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23 pages, 4810 KiB

Open AccessArticle

Classification of Hemodynamics Scenarios from a Public Radar Dataset Using a Deep Learning Approach

by Gašper Slapničar, Wenjin Wang and Mitja Luštrek

Sensors 2021, 21(5), 1836; https://doi.org/10.3390/s21051836 - 6 Mar 2021

Cited by 7 | Viewed by 3376

Abstract

Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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17 pages, 3743 KiB

Open AccessArticle

Motion-Tolerant Non-Contact Heart-Rate Measurements from Radar Sensor Fusion

by Yu Rong, Arindam Dutta, Alex Chiriyath and Daniel W. Bliss

Sensors 2021, 21(5), 1774; https://doi.org/10.3390/s21051774 - 4 Mar 2021

Cited by 28 | Viewed by 4943

Abstract

Microwave radar technology is very attractive for ubiquitous short-range health monitoring due to its non-contact, see-through, privacy-preserving and safe features compared to the competing remote technologies such as optics. The possibility of radar-based approaches for breathing and cardiac sensing was demonstrated a few decades ago. However, investigation regarding the robustness of radar-based vital-sign monitoring (VSM) is not available in the current radar literature. In this paper, we aim to close this gap by presenting an extensive experimental study of vital-sign radar approach. We consider diversity in test subjects, fitness levels, poses/postures, and, more importantly, random body movement (RBM) in the study. We discuss some new insights that lead to robust radar heart-rate (HR) measurements. A novel active motion cancellation signal-processing technique is introduced, exploiting dual ultra-wideband (UWB) radar system for motion-tolerant HR measurements. Additionally, we propose a spectral pruning routine to enhance HR estimation performance. We validate the proposed method theoretically and experimentally. Totally, we record and analyze about 3500 seconds of radar measurements from multiple human subjects. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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32 pages, 1082 KiB

Open AccessEditor’s ChoiceReview

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

by Chun-Hong Cheng, Kwan-Long Wong, Jing-Wei Chin, Tsz-Tai Chan and Richard H. Y. So

Sensors 2021, 21(18), 6296; https://doi.org/10.3390/s21186296 - 20 Sep 2021

Cited by 64 | Viewed by 16240

Abstract

Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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16 pages, 4496 KiB

Open AccessReview

Noncontact Respiratory Monitoring Using Depth Sensing Cameras: A Review of Current Literature

by Anthony P. Addison, Paul S. Addison, Philip Smit, Dominique Jacquel and Ulf R. Borg

Sensors 2021, 21(4), 1135; https://doi.org/10.3390/s21041135 - 6 Feb 2021

Cited by 32 | Viewed by 4738

Abstract

There is considerable interest in the noncontact monitoring of patients as it allows for reduced restriction of patients, the avoidance of single-use consumables and less patient–clinician contact and hence the reduction of the spread of disease. A technology that has come to the fore for noncontact respiratory monitoring is that based on depth sensing camera systems. This has great potential for the monitoring of a range of respiratory information including the provision of a respiratory waveform, the calculation of respiratory rate and tidal volume (and hence minute volume). Respiratory patterns and apneas can also be observed in the signal. Here we review the ability of this method to provide accurate and clinically useful respiratory information. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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15 pages, 4172 KiB

Open AccessLetter

Non-Contact Measurements of Electrocardiogram and Cough-Associated Electromyogram from the Neck Using In-Pillow Common Cloth Electrodes: A Proof-of-Concept Study

by Akira Takano, Hiroshi Ishigami and Akinori Ueno

Sensors 2021, 21(3), 812; https://doi.org/10.3390/s21030812 - 26 Jan 2021

Cited by 8 | Viewed by 4237

Abstract

Asthma and chronic obstructive pulmonary disease are associated with nocturnal cough and changes in heart rate. In this work, the authors propose a proof-of-concept non-contact system for performing capacitive electrocardiogram (cECG) and cough-associated capacitive electromyogram (cEMG) measurements using cloth electrodes under a pillowcase. Two electrodes were located along with the approximate vector of lead II ECG and were used for both cECG and cEMG measurements. A signature voltage follower was introduced after each electrode to detect biopotentials with amplitudes of approximately 100 µV. A bootstrapping technique and nonlinear electrical component were combined and implemented in the voltage follower to attain a high input impedance and rapid static discharge. The measurement system was evaluated in a laboratory experiment for seven adult males and one female (average age: 22.5 ± 1.3 yr). The accuracy of R-wave detection for 2-min resting periods was 100% in six subjects, with an overall average of 87.5% ± 30.0%. Clearly visible cEMGs were obtained for each cough motion for all subjects, synchronized with reference EMGs from submental muscle. Although there remains room for improvement in practical use, the proposed system is promising for unobtrusive detection of heart rate and cough over a prolonged period of time. Full article

(This article belongs to the Special Issue Contactless Sensors for Healthcare)

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