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Monitoring Technologies in Healthcare Applications

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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 (20 September 2022) | Viewed by 22971

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Special Issue Editors

Dr. Ignacio Oropesa

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Guest Editor

Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Centre for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: video-based tracking; biomedical signal processing; virtual reality; augmented reality machine/deep learning; wearables; natural language processing
Special Issues, Collections and Topics in MDPI journals

Dr. Patricia Sánchez-González

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Guest Editor

Biomedical Engineering and Telemedicine Centre, ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: virtual reality; computer vision; medical education and assessment; patient rehabilitation
Special Issues, Collections and Topics in MDPI journals

Dr. Magdalena Karolina Chmarra

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Guest Editor

Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering (3mE), Delft University of Technology, Van der Maasweg 9, 2629HZ Delft, The Netherlands
Interests: Training; Assessment; medical technology; laparoscopy; image-guided interventions

Special Issue Information

Dear Colleagues,

Advances in sensing and monitoring technologies have radically increased our capacity to tap new sources of information and measure, process, and analyze vast quantities of data. The healthcare sector is one of the great beneficiaries of this revolution.

The monitoring of patients by means of unobtrusive and/or wearable devices can provide useful information on the evolution of a chronic disease and on ongoing treatment. It can also provide the grounds for lifestyle interventions aimed at creating healthy habits. Moreover, large cohort studies can help increase our capacity to understand, predict, and minimize the risk of developing certain conditions.

Sensing technologies can also improve healthcare management and education. For example, tracking technologies can be used in surgical simulators to measure dexterity skills. Monitoring of professionals’ stress responses can be useful in preventing burnouts and fatigue. Surgical processes may be improved through the development of smart operating rooms.

In this Special Issue of Sensors, we ask for original research articles focused on healthcare applications.

Contributions may focus on the preliminary feasibility of applied innovative sensing technologies or novel applications for existing ones. Studies should reflect on both the advantages and safety implications of the proposed solutions. They may cover different stages of technological and clinical validation. Contributions from various sensing technologies are welcome, as long as the focus of the application is on monitoring for healthcare applications.

Dr. Ignacio Oropesa
Dr. Patricia Sánchez-González
Dr. Magdalena Karolina Chmarra
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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

management of chronic diseases
lifestyle interventions
rehabilitation
monitoring of follow-up clinical interventions
medical and surgical training
smart operating room

Published Papers (9 papers)

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Research

21 pages, 10377 KiB

Open AccessArticle

Breathing Pattern Monitoring by Using Remote Sensors

by Janosch Kunczik, Kerstin Hubbermann, Lucas Mösch, Andreas Follmann, Michael Czaplik and Carina Barbosa Pereira

Sensors 2022, 22(22), 8854; https://doi.org/10.3390/s22228854 - 16 Nov 2022

Cited by 7 | Viewed by 2735

Abstract

The ability to continuously and unobtrusively monitor and classify breathing patterns can be very valuable for automated health assessments because respiration is tightly coupled to many physiological processes. Pathophysiological changes in these processes often manifest in altered breathing patterns and can thus be immediately detected. In order to develop a breathing pattern monitoring system, a study was conducted in which volunteer subjects were asked to breathe according to a predefined breathing protocol containing multiple breathing patterns while being recorded with color and thermal cameras. The recordings were used to develop and compare several respiratory signal extraction algorithms. An algorithm for the robust extraction of multiple respiratory features was developed and evaluated, capable of differentiating a wide range of respiratory patterns. These features were used to train a one vs. one multiclass support vector machine, which can distinguish between breathing patterns with an accuracy of

95.79

%. The recorded dataset was published to enable further improvement of contactless breathing pattern classification, especially for complex breathing patterns. Full article

(This article belongs to the Special Issue Monitoring Technologies in Healthcare Applications)

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20 pages, 5845 KiB

Open AccessArticle

Design and Haemodynamic Analysis of a Novel Anchoring System for Central Venous Pressure Measurement

by Tejaswini Manavi, Masooma Ijaz, Helen O’Grady, Michael Nagy, Jerson Martina, Ciaran Finucane, Faisal Sharif and Haroon Zafar

Sensors 2022, 22(21), 8552; https://doi.org/10.3390/s22218552 - 6 Nov 2022

Cited by 2 | Viewed by 2535

Abstract

Background/Objective: In recent years, treatment of heart failure patients has proved to benefit from implantation of pressure sensors in the pulmonary artery (PA). While longitudinal measurement of PA pressure profoundly improves a clinician’s ability to manage HF, the full potential of central venous pressure as a clinical tool has yet to be unlocked. Central venous pressure serves as a surrogate for the right atrial pressure, and thus could potentially predict a wider range of heart failure conditions. However, it is unclear if current sensor anchoring methods, designed for the PA, are suitable to hold pressure sensors safely in the inferior vena cava. The purpose of this study was to design an anchoring system for accurate apposition in inferior vena cava and evaluate whether it is a potential site for central venous pressure measurement. Materials and Methods: A location inferior to the renal veins was selected as an optimal site based on a CT scan analysis. Three anchor designs, a 10-strut anchor, and 5-struts with and without loops, were tested on a custom-made silicone bench model of Vena Cava targeting the infra-renal vena cava. The model was connected to a pulsatile pump system and a heated water bath that constituted an in-vitro simulation unit. Delivery of the inferior vena cava implant was accomplished using a preloaded introducer and a dilator as a push rod to deploy the device at the target area. The anchors were subjected to manual compression tests to evaluate their stability against dislodgement. Computational Fluid Dynamics (CFD) analysis was completed to characterize blood flow in the anchor’s environment using pressure-based transient solver. Any potential recirculation zones or disturbances in the blood flow caused by the struts were identified. Results: We demonstrated successful anchorage and deployment of the 10-strut anchor in the Vena Cava bench model. The 10-strut anchor remained stable during several compression attempts as compared with the other two 5-strut anchor designs. The 10-strut design provided the maximum number of contact points with the vessel in a circular layout and was less susceptible to movement or dislodgement during compression tests. Furthermore, the CFD simulation provided haemodynamic analysis of the optimum 10-strut anchor design. Conclusions: This study successfully demonstrated the design and deployment of an inferior vena cava anchoring system in a bench test model. The 10-strut anchor is an optimal design as compared with the two other 5-strut designs; however, substantial in-vivo experiments are required to validate the safety and accuracy of such implants. The CFD simulation enabled better understanding of the haemodynamic parameters and any disturbances in the blood flow due to the presence of the anchor. The ability to place a sensor technology in the vena cava could provide a simple and minimally invasive approach for heart failure patients. Full article

(This article belongs to the Special Issue Monitoring Technologies in Healthcare Applications)

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18 pages, 16352 KiB

Open AccessArticle

Toward an Automatic Assessment of Cognitive Dysfunction in Relapsing–Remitting Multiple Sclerosis Patients Using Eye Movement Analysis

by Cecilia E. García Cena, David Gómez-Andrés, Irene Pulido-Valdeolivas, Victoria Galán Sánchez-Seco, Angela Domingo-Santos, Sara Moreno-García and Julián Benito-León

Sensors 2022, 22(21), 8220; https://doi.org/10.3390/s22218220 - 27 Oct 2022

Cited by 4 | Viewed by 1858

Abstract

Despite the importance of cognitive function in multiple sclerosis, it is poorly represented in the Expanded Disability Status Scale (EDSS), the commonly used clinical measure to assess disability, suggesting that an analysis of eye movement, which is generated by an extensive and well-coordinated functional network that is engaged in cognitive function, could have the potential to extend and complement this more conventional measure. We aimed to measure the eye movement of a case series of MS patients with relapsing–remitting MS to assess their cognitive status using a conventional gaze tracker. A total of 41 relapsing–remitting MS patients and 43 age-matched healthy controls were recruited for this study. Overall, we could not find a clear common pattern in the eye motor abnormalities. Vertical eye movement was more impaired in MS patients than horizontal movement. Increased latencies were found in the prosaccades and reflexive saccades of antisaccade tests. The smooth pursuit was impaired with more corrections (backup and catchup movements,

p < 0.01

). No correlation was found between eye movement variables and EDSS or disease duration. Despite significant alterations in the behavior of the eye movements in MS patients, which are compatible with altered cognitive status, there is no common pattern of these alterations. We interpret this as a consequence of the patchy, heterogeneous distribution of white matter involvement in MS that provokes multiple combinations of impairment at different points in the different networks involved in eye motor control. Further studies are therefore required. Full article

(This article belongs to the Special Issue Monitoring Technologies in Healthcare Applications)

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16 pages, 3369 KiB

Open AccessArticle

Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor

by Pragya Sharma, Zijing Zhang, Thomas B. Conroy, Xiaonan Hui and Edwin C. Kan

Sensors 2022, 22(20), 8047; https://doi.org/10.3390/s22208047 - 21 Oct 2022

Cited by 2 | Viewed by 2177

Abstract

This work presents a study on users’ attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user’s baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectively Full article

(This article belongs to the Special Issue Monitoring Technologies in Healthcare Applications)

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22 pages, 2376 KiB

Open AccessArticle

An Automatic System for Continuous Pain Intensity Monitoring Based on Analyzing Data from Uni-, Bi-, and Multi-Modality

by Ehsan Othman, Philipp Werner, Frerk Saxen, Marc-André Fiedler and Ayoub Al-Hamadi

Sensors 2022, 22(13), 4992; https://doi.org/10.3390/s22134992 - 1 Jul 2022

Cited by 7 | Viewed by 2102

Abstract

Pain is a reliable indicator of health issues; it affects patients’ quality of life when not well managed. The current methods in the clinical application undergo biases and errors; moreover, such methods do not facilitate continuous pain monitoring. For this purpose, the recent methodologies in automatic pain assessment were introduced, which demonstrated the possibility for objectively and robustly measuring and monitoring pain when using behavioral cues and physiological signals. This paper focuses on introducing a reliable automatic system for continuous monitoring of pain intensity by analyzing behavioral cues, such as facial expressions and audio, and physiological signals, such as electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) from the X-ITE Pain Dataset. Several experiments were conducted with 11 datasets regarding classification and regression; these datasets were obtained from the database to reduce the impact of the imbalanced database problem. With each single modality (Uni-modality) experiment, we used a Random Forest [RF] baseline method, a Long Short-Term Memory (LSTM) method, and a LSTM using a sample weighting method (called LSTM-SW). Further, LSTM and LSTM-SW were used with fused modalities (two modalities = Bi-modality and all modalities = Multi-modality) experiments. Sample weighting was used to downweight misclassified samples during training to improve the performance. The experiments’ results confirmed that regression is better than classification with imbalanced datasets, EDA is the best single modality, and fused modalities improved the performance significantly over the single modality in 10 out of 11 datasets. Full article

(This article belongs to the Special Issue Monitoring Technologies in Healthcare Applications)

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11 pages, 21410 KiB

Open AccessArticle

Measuring Heart Rate Variability Using Facial Video

by Gerardo H. Martinez-Delgado, Alfredo J. Correa-Balan, José A. May-Chan, Carlos E. Parra-Elizondo, Luis A. Guzman-Rangel and Antonio Martinez-Torteya

Sensors 2022, 22(13), 4690; https://doi.org/10.3390/s22134690 - 21 Jun 2022

Cited by 6 | Viewed by 3169

Abstract

Heart Rate Variability (HRV) has become an important risk assessment tool when diagnosing illnesses related to heart health. HRV is typically measured with an electrocardiogram; however, there are multiple studies that use Photoplethysmography (PPG) instead. Measuring HRV with video is beneficial as a non-invasive, hands-free alternative and represents a more accessible approach. We developed a methodology to extract HRV from video based on face detection algorithms and color augmentation. We applied this methodology to 45 samples. Signals obtained from PPG and video recorded an average mean error of less than 1 bpm when measuring the heart rate of all subjects. Furthermore, utilizing PPG and video, we computed 61 variables related to HRV. We compared each of them with three correlation metrics (i.e., Kendall, Pearson, and Spearman), adjusting them for multiple comparisons with the Benjamini–Hochberg method to control the false discovery rate and to retrieve the q-value when considering statistical significance lower than 0.5. Using these methods, we found significant correlations for 38 variables (e.g., Heart Rate, 0.991; Mean NN Interval, 0.990; and NN Interval Count, 0.955) using time-domain, frequency-domain, and non-linear methods. Full article

(This article belongs to the Special Issue Monitoring Technologies in Healthcare Applications)

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13 pages, 2941 KiB

Open AccessArticle

Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study

by Saeed Ali Alsareii, Mohsin Raza, Abdulrahman Manaa Alamri, Mansour Yousef AlAsmari, Muhammad Irfan, Umar Khan and Muhammad Awais

Sensors 2022, 22(4), 1420; https://doi.org/10.3390/s22041420 - 12 Feb 2022

Cited by 7 | Viewed by 2133

Abstract

Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring. Full article

(This article belongs to the Special Issue Monitoring Technologies in Healthcare Applications)

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19 pages, 2365 KiB

Open AccessArticle

Correlating Personal Resourcefulness and Psychomotor Skills: An Analysis of Stress, Visual Attention and Technical Metrics

by Carmen Guzmán-García, Patricia Sánchez-González, Juan A. Sánchez Margallo, Nicola Snoriguzzi, José Castillo Rabazo, Francisco M. Sánchez Margallo, Enrique J. Gómez and Ignacio Oropesa

Sensors 2022, 22(3), 837; https://doi.org/10.3390/s22030837 - 22 Jan 2022

Cited by 3 | Viewed by 2556

Abstract

Modern surgical education is focused on making use of the available technologies in order to train and assess surgical skill acquisition. Innovative technologies for the automatic, objective assessment of nontechnical skills are currently under research. The main aim of this study is to determine whether personal resourcefulness can be assessed by monitoring parameters that are related to stress and visual attention and whether there is a relation between these and psychomotor skills in surgical education. For this purpose, we implemented an application in order to monitor the electrocardiogram (ECG), galvanic skin response (GSR), gaze and performance of surgeons-in-training while performing a laparoscopic box-trainer task so as to obtain technical and personal resourcefulness’ metrics. Eight surgeons (6 nonexperts and 2 experts) completed the experiment. A total of 22 metrics were calculated (7 technical and 15 related to personal resourcefulness) per subject. The average values of these metrics in the presence of stressors were compared with those in their absence and depending on the participants’ expertise. The results show that both the mean normalized GSR signal and average surgical instrument’s acceleration change significantly when stressors are present. Additionally, the GSR and acceleration were found to be correlated, which indicates that there is a relation between psychomotor skills and personal resourcefulness. Full article

(This article belongs to the Special Issue Monitoring Technologies in Healthcare Applications)

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15 pages, 1976 KiB

Open AccessArticle

Effect of BoNT/A in the Surface Electromyographic Characteristics of the Pelvic Floor Muscles for the Treatment of Chronic Pelvic Pain

by Monica Albaladejo-Belmonte, Francisco J. Nohales-Alfonso, Marta Tarazona-Motes, Maria De-Arriba, Jose Alberola-Rubio and Javier Garcia-Casado

Sensors 2021, 21(14), 4668; https://doi.org/10.3390/s21144668 - 7 Jul 2021

Cited by 4 | Viewed by 2314

Abstract

Chronic pelvic pain (CPP) is a complex condition with a high economic and social burden. Although it is usually treated with botulinum neurotoxin type A (BoNT/A) injected into the pelvic floor muscles (PFM), its effect on their electrophysiological condition is unknown. In this study, 24 CPP patients were treated with BoNT/A. Surface electromyographic signals (sEMG) were recorded at Weeks 0 (infiltration), 8, 12 and 24 from the infiltrated, non-infiltrated, upper and lower PFM. The sEMG of 24 healthy women was also recorded for comparison. Four parameters were computed: root mean square (RMS), median frequency (MDF), Dimitrov’s index (DI) and sample entropy (SampEn). An index of pelvic electrophysiological impairment (IPEI) was also defined with respect to the healthy condition. Before treatment, the CPP and healthy parameters of almost all PFM sides were significantly different. Post-treatment, there was a significant reduction in power (<RMS), a shift towards higher frequencies (>MDF), lower fatigue index (<DI) and increased information complexity (>SampEn) in all sites in patients, mainly during PFM contractions, which brought their electrophysiological condition closer to that of healthy women (<IPEI). sEMG can be used to assess the PFM electrophysiological condition of CPP patients and the effects of therapies such as BoNT/A infiltration. Full article

(This article belongs to the Special Issue Monitoring Technologies in Healthcare Applications)

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