Vital Sign Measurements Research Articles

Vital signs in pediatric oncology patients assessed by continuous recording with a wearable device, NCT04134429

Pediatric patients with cancer are at high risk for severe infections. Changes in vital signs, triggered by infections, may be detected earlier by continuous recording with a wearable device than with discrete measurements. This prospective, observational single-center feasibility study consecutively recruited pediatric patients undergoing chemotherapy for cancer. The WD Everion® was used for 14 days in each of the 20 patients on study to continuously record vital signs. Nine different vital signs and health indicators derived from them, plus six quality scores. This resulted in 274 study days (6576 hours) with 85’854 measuring points, which are a total of 772’686 measurements of vital signs and health indicators, plus 515’124 quality scores. Additionally, non-WD data like side effects, acceptability of the WD and effort for investigators were collected. In this manuscript, we present the methods of acquisition and explanations to the complete data set, which have been made publically available on open access and which can be used to study feasibility of continuous multi-parameter recording of vital signs by a WD.

Wearable Sensors for Vital Signs Measurement: A Survey

With the outbreak of coronavirus disease-2019 (COVID-19) worldwide, developments in the medical field have aroused concerns within society. As science and technology develop, wearable medical sensors have become the main means of medical data acquisition. To analyze the intelligent development status of wearable medical sensors, the current work classifies and prospects the application status and functions of wireless communication wearable medical sensors, based on human physiological data acquisition in the medical field. By understanding its working principles, data acquisition modes and action modes, the work chiefly analyzes the application of wearable medical sensors in vascular infarction, respiratory intensity, body temperature, blood oxygen concentration, and sleep detection, and reflects the key role of wearable medical sensors in human physiological data acquisition. Further exploration and prospecting are made by investigating the improvement of information security performance of wearable medical sensors, the improvement of biological adaptability and biodegradability of new materials, and the integration of wearable medical sensors and intelligence-assisted rehabilitation. The research expects to provide a reference for the intelligent development of wearable medical sensors and real-time monitoring of human health in the follow-up medical field.

Wearable devices to monitor recovery after abdominal surgery: scoping review

BackgroundWearable devices have been proposed as a novel method for monitoring patients after surgery to track recovery, identify complications early, and improve surgical safety. Previous studies have used a heterogeneous range of devices, methods, and analyses. This review aimed to examine current methods and wearable devices used for monitoring after abdominal surgery and identify knowledge gaps requiring further investigation.MethodsA scoping review was conducted given the heterogeneous nature of the evidence. MEDLINE, EMBASE, and Scopus databases were systematically searched. Studies of wearable devices for monitoring of adult patients within 30 days after abdominal surgery were eligible for inclusion.ResultsA total of 78 articles from 65 study cohorts, with 5153 patients were included. Thirty-one different wearable devices were used to measure vital signs, physiological measurements, or physical activity. The duration of postoperative wearable device use ranged from 15 h to 3 months after surgery. Studies mostly focused on physical activity metrics (71.8 per cent). Continuous vital sign measurement and physical activity tracking both showed promise for detecting postoperative complications earlier than usual care, but conclusions were limited by poor device precision, adherence, occurrence of false alarms, data transmission problems, and retrospective data analysis. Devices were generally well accepted by patients, with high levels of acceptance, comfort, and safety.ConclusionWearable technology has not yet realized its potential to improve postoperative monitoring. Further work is needed to overcome technical limitations, improve precision, and reduce false alarms. Prospective assessment of efficacy, using an intention-to-treat approach should be the focus of further studies.

An Overview of Smartphone Apps


 Health-based mobile applications (mHealth) are downloadable applications on a smartphone or similar device for use in health care, either by the person directly or by a health care provider. This Horizon Scan summarizes the available information and provides an overview of health apps on smartphones that are not connected to specialized medical equipment, describing examples of emerging apps in different clinical areas, who they might benefit, and their operational issues.
 There are over 350,000 mobile applications available for download in app stores, used for a variety of disease areas. These areas include chronic disease, stress, mental health, fitness, sleeping problems, general medication adherence and tracking, and vital sign measurements.
 The scan identified that health apps often fall into 1 of 4 categories: informational applications, diagnostic applications, disease management applications, and fitness tracking applications.
 Numerous apps were identified that are available for use by people in Canada. Health apps offer the potential to provide convenience, flexibility, accessibility, and personalized health information. However, the majority of health apps that require evidence of benefit for users have not been assessed in appropriately designed studies that examine their clinical efficacy, or safety. The apps that have been tested in some research studies may have numerous shortcomings in areas such as app design, user engagement, user satisfaction, and retention.
 This scan describes some operational considerations for apps that relate to their lack of evidence-base, concerns about biases in app design, and the need for equity focused app development. It was noted in the literature that many apps do not provide appropriate privacy and confidentiality for consumers, which may put people at risk of data breaches or inappropriate use of personal data.

Improving the user experience of televisits and telemonitoring for heart failure patients in less than 6 months: a methodological approach

BackgroundWith the spread of COVID-19, telemedicine solutions became crucial to release continuous and remote assistance to chronic patients. The rapid transition to telemedicine solutions did not allow a complete assessment of the user experience by both patients and medical personnel. Despite the well-known benefits in remote care, the lack of usability evaluation of already existing technologies for the vital signs measurement has emerged. A telemedicine platform must match video communication between patients and the medical staff with the possibility to measure vital parameters. Furthermore, technological assistance may overcome the unfamiliarity with telemedicine and drastically reduce the learning time for both patients and medical personnel. ObjectivesThe research work presents a method to improve the user experience of a telemedicine service based on the combination of televisits and telemonitoring with wearable sensors for heart failure patients. Skilled technological staff is proposed by the presented method to lead the learning process of both medical personnel and patients in order to reach a high level of usability in less than 6 months. MethodsThe proposed method is composed of 5 steps: identification of technological staff and end users; selection of the telemedicine platform; design of questionnaires for usability analysis; training of clinicians and patients; final usability evaluation of the telemedicine platform by means of customized satisfaction questionnaires and Post-Study System Usability Questionnaires (PSSUQs). The methodological approach has been tested in collaboration with a heart failure clinic by involving 5 physicians, 5 nurses, 15 heart failure patients, a research engineer and 4 technicians. A platform with 3 wearable sensors has been chosen: a wireless thermometer, a finger pulse-oximeter and an undershirt have been used for the detection of ECG trace. While these devices allowed asynchronous measurements of physiological data, scheduled televisits have been used for direct communication between physicians and patients. ResultsSatisfaction questionnaires of patients and clinicians reached respectively 65.18% and 65.83%, while PSSUQ scores were respectively 91.73% and 81.70%. Both groups of end users confirmed a good level of usability and their satisfaction about the ease of use and the perceived quality of the instrumentation. Moreover, 73% of patients did not require help from caregivers to use the kit of sensors. The results have been reached in 5.5 months according to the aim defined initially. Research engineers have played a crucial role in helping clinicians and patients to improve the user experience with the telemedicine platform. ConclusionsThe high level of usability and satisfaction confirmed that the proposed methodological approach helps to learn the technological features of the telemedicine platforms, which are based on different types of technology, such as web applications, wearable sensors and virtual calls. The positive results with heart failure patients encouraged to plan further research studies by using the designed method with other categories of chronic patients.

Vital Signs Monitoring Device with BPM and SpO2 Notification Using Telegram Application Based on Thinger.io Platform

Vital signs are an important component of monitoring the adult or child patient's progress during hospitalization, as they allow for the prompt detection of delayed recovery or adverse events. Vital signs are measured to obtain basic indicators of a patient's health status. The most common intervention performed in hospital medicine is a measurement of vital signs, and these traditionally consist of blood pressure, temperature, pulse rate, and respiratory rate. Advanced monitoring systems incorporate a balanced combination of clinical and technological aspects to give an innovative healthcare outcome. Remote patient monitoring systems are rapidly becoming the core of healthcare deliveries. The paradigm shifted from traditional and manual recording to computer-based electronic records and further to smartphones as versatile and innovative healthcare monitoring systems. This research aims to design a Vital Sign Monitoring device for BPM and SpO2 Parameters with Notifications through the IoT-Based Telegram application. This device can monitor vital signs, especially BPM and SPO2, wherever the patient is and whenever so that doctors or health workers and patients can find out their health condition. This display can be viewed via web thinger.io, then forwarded to telegram if an abnormal patient condition is found, and there is an indicator light that will light up differently for each condition. This study uses the MAX30100, which is a digital sensor to detect oxygen saturation and heart rate. The results of this study have succeeded in displaying data on the IoT web and sending notifications to the Telegram application. And also, the resulting data has an error that does not exceed the allowable limit according to each parameter. The difference between heart rate readings and oxygen saturation values ​​on the device and patient monitor is 0.015% for heart rate and 0.01% for oxygen saturation. This study indicates that it is time to monitor vital signs that can be seen remotely and have a system that is an inexpensive and easy-to-operate device for health workers without interfering with activities of daily living.

Telehealth modulation and new proposals during the Covid-19 pandemic: a literature review.

The Covid-19 pandemic has completely modified the Healthcare organization. This review aims to analyze the evolution of the different Telemedicine areas during pandemic. Electronic Health Records allows accelerating the study of patients suffering from Covid-19 disease, supporting their clinical assistance. The decreasing rehabilitation programs have determined a deterioration of the patient quality of life. Teleradiology was necessary to discard the increased requests and dab the shortage of staff, and to guarantee the interaction between specialist and patient. Telecardiology was fundamental determining a reduced of secondary mortality for cardiological complications of the infection. Teledermatology has permitted an early identification of the patients affected through diagnoses of cutaneous signs, reducing clinical visits. Telelegal-medicine changed through a law, that was introduced allowing a remarkable use of videoconferencing in the different stages of judicial and extrajudicial process. The digital consultations and home drug delivery were implemented in telepharmacy area. Artificial Intelligence allows an early diagnosis of the infection, monitoring the treatment through an intelligent platform. Robotic assisted telemedicine minimizes the risk of exposure allowing the disinfection of the places, drugs and meals delivery, the measurement of vital signs. Mobile Health facilitated the collection and the automatic transfer of the patient's parameters. Telemedicine would constitute still today as complementary but not substitutive to the traditional medicine. During the pandemic telemedicine has resulted important to guarantee continuity cures. Radiology and Dermatology showed a major telemedicine application.

"Help Me Sleep": A Quality Initiative to Reduce Overnight Vital Signs.

Sleep is an essential part of the recovery process, yet inpatient sleep quality is poor. Patients and families report that vital signs are the most bothersome overnight disruption. Obtaining vital signs every 4 hours (Q4H) is not evidence-based and is frequently ordered indiscriminately. We aimed to decrease the percentage of patient nights with vital sign checks between 12 am and 6 am in a low-risk population from 98% to 70% within 12 months to minimize overnight sleep disruptions and improve inpatient sleep. We conducted a quality improvement project on 3 pediatric hospital medicine teams at a large free-standing children's hospital. Our multidisciplinary team defined low-risk patients as those admitted for hyperbilirubinemia and failure to thrive. Interventions were focused around education, electronic health record decision support, and patient safety. The outcome measure was the percentage of patient nights without a vital sign measurement between 12 am and 6 am and was analyzed by using statistical process control charts. Our process measure was the use of an appropriate vital sign order. Balancing measures included adverse patient events, specifically code blues outside the ICU and emergent transfers. From March 2020 to April 2021, our pediatric hospital medicine (PHM) services admitted 449 low-risk patients for a total of 1550 inpatient nights. The percentage of patient nights with overnight vital signs decreased from 98% to 38%. There were no code blues or emergent transfers. Our improvement interventions reduced the frequency of overnight vital sign monitoring in 2 low-risk groups without any adverse events.

Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis

To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4998 algorithmic operations from 11 mathematical families to random daily 10 min segments from 5957 NICU infants, 205 of whom died. We clustered the results and selected a representative from each, and examined multivariable logistic regression models. 3555 operations were usable; 20 cluster medoids held more than 81% of the information, and a multivariable model had AUC 0.83. New algorithms outperformed others: moving threshold, successive increases, surprise, and random walk. We computed provenance of the computations and constructed a software library with links to the data. We conclude that highly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week.

Continuous vital signs monitors: a replacement for traditional vital signs?

Lightweight, wireless vital signs monitors offer the ability to continuously monitor ward-based patients, which could revolutionise identification of patient deterioration. Continuous vital signs require correlation to traditional vital signs measurements in acutely unwell inpatients if we are to understand how best to integrate continuous data into track-and-trigger systems. We report data collected during Continuous Signs Monitoring in COVID-19 Patients (COSMIC-19) (clinicaltrials.gov: NCT04581031), a feasibility study of continuous vital signs data recorded using wearable sensors in hospitalised patients with COVID-19.

Self-perceived quality of sleep among COPD patients in Greece: the SLEPICO study

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide accompanied by a substantial social and economic burden for the patient and the society. Poor sleep quality among COPD patients is frequently unnoticed and unaddressed by physicians and patients themselves, although it is a major source of further deterioration of these patients’ quality of life. The aim of the present study was to record the quality of sleep in COPD patients among the Greek population and correlate these findings with various features of these patients, using the COPD and Asthma Sleep Impact Scale (CASIS). This was a cross-sectional observational study. Forty different variables (demographics, vital sign measurements, COPD-related medical history parameters, comorbidities, CASIS questionnaire results, COPD assessment test, COPD severity based on spirometry measurements, COPD stage based on the ABCD assessment approach, inhaled COPD treatment report) were collected from 3454 nation-wide COPD patients (Greece). The study sample consisted of COPD patients, mainly male (73%) with a median age of 69 years and a median BMI of 27.2. More than half of COPD patients (60.6%) suffered from moderate disease severity and 23.8% from severe disease, while less than half (42.1%) suffered from at least one exacerbation of the disease over the last year prior study enrollment. About 14% reported frequent to very frequent issues affecting their sleep quality, between a fourth and a third of them reported occasional night sleep disturbances, and at least half of them reported no or very infrequent problems in their night sleep. Our study indicates that the COPD assessment test (CAT) and the spirometry-based disease severity can predict the poorness in the quality of sleep (F2,3451 = 1397.5, p < 0.001, adj. R2 = 0.45) as assessed by CASIS score, and that the latter also correlates with age (ρ = 0.122, p < 0.001) and disease duration (ρ = 0.104, p < 0.001). On the contrary, there appears to be no correlation between sleep quality and number of exacerbations. Finally, untreated patients with COPD suffer from poorer quality of sleep compared to treated subjects, independently of the use of inhaled corticosteroids (F2,3451 = 21.65, p < 0.001). The results of the SLEPICO study show that increased age, prolonged disease duration, and especially CAT score ≥ 10, and severe COPD stage, might act as important indicators for deterioration in the quality of sleep, with potential consequences in the daily routine of those patients, thus urging potentially for further pharmacological interventions or modifications.

Integrating multimodal data through interpretable heterogeneous ensembles.

MotivationIntegrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems.ResultsWe propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling.Availability and implementationCode and data are available at https://github.com/GauravPandeyLab/ensemble_integration.Supplementary information Supplementary data are available at Bioinformatics Advances online.

Effect of a Wireless Vital Sign Monitoring System on the Rapid Response System in the General Ward

While wireless vital sign monitoring is expected to reduce the vital sign measurement time (thus reducing the nursing workload), its impact on the rapid response system is unclear. This study compared the time from vital sign measurement to recording and rapid response system activation between wireless and conventional vital sign monitoring in the general ward, to investigate the impact of wireless vital sign monitoring system on the rapid response system. The study divided 249 patients (age > 18 years; female: 47, male: 202) admitted to the general ward into non-wireless (n = 101) and wireless (n = 148) groups. Intervals from vital sign measurement to recording and from vital sign measurement to rapid response system activation were recorded. Effects of wireless system implementation for vital sign measurement on the nursing workload were surveyed in 30 nurses. The interval from vital sign measurement to recording was significantly shorter in the wireless group than in the non-wireless group (4.3 ± 2.9 vs. 44.7 ± 14.4 min, P < 0.001). The interval from vital sign measurement to rapid response system activation was also significantly lesser in the wireless group than in the non-wireless group (27.5 ± 12.9 vs. 41.8 ± 19.6 min, P = 0.029). The nursing workload related to vital sign measurement significantly decreased from 3 ± 0.87 to 2.4 ± 9.7 (P = 0.021) with wireless system implementation. Wireless vital sign monitoring significantly reduced the time to rapid response system activation by shortening the time required to measure the vital signs. It also significantly reduced the nursing workload.

Opieka nad pacjentem po zabiegu transplantacji serca – studium przypadku

Heart transplantation is a recognized and effective method of treating end-stage heart failure. Complicated heart transplant surgery carries a high surgical risk as well as complications that are dangerous to life and health. Transplantation is a life-saving opportunity for patients who have run out of alternative treatments. According to Poltransplant data, transplantation was performed in Poland in the last year 2021 200 hearts. Patients with heart transplants face many physical and psychological problems every day. The aim of the study: to diagnose and discuss nursing problems occurring in a patient 3.5 years after heart transplantation, to determine the impact of the organ transplantation on the functioning of the individual in everyday life, and to present a care plan and its results, based on the applied nursing activities. Material and methods: The study uses the research method - an individual case study in which research techniques such as interview, observation, analysis of medical records and measurement of vital signs were used. The selected methods made it possible to develop an individual nursing care plan for the research subject - a 50-year-old patient, three and a half years after the transplant. Results: After the interview, observation and analysis of medical records and as a result of the measurements made, the patient was diagnosed with nursing problems, including such as: increased susceptibility to infections that may pose a serious threat to the life and health of the patient caused by the weakening of the immune system by immunosuppression, anxiety and fear related to the risk of rejection of a transplanted organ and mood instability resulting from uncertain health situation and the effects of immunosuppressive drugs, physical limitations related to with previous operations. Conclusions: Despite the passage of time, the patient after heart transplantation still feels the fear of a sudden deterioration of his health, the occurrence of side effects of the implemented treatment, and to the greatest extent his organism rejects the transplanted organ. Educating the patient, caring for it and keeping track of his health condition makes him feel more confident and at ease. The quality of life of the patient after transplantation can be significantly improved thanks to discipline and properly conducted self-care and self-care.

Demonstration of relative bioavailability of newly developed and innovator drug metaxalone 800 mg in healthy subjects under fed condition

The notable unbiased of this research work was to evaluate the well-being and effectiveness of metaxalone by administering the newly developed test and reference drug. A two-period, two-categorization, crossover bioavailability study in fed conditions. Eleven participants were dosed and completed the trial successfully. The drugs were administered by way of a schedule. Samples collected in both periods for pharmacokinetic evaluation. Plasma samples analyzed using a validated method. Pharmacokinetic parameters for investigational and reference products were calculated using the metaxalone drug concentration and safety of the participants monitored by measurement of vital sign. Relative estimation factors calculated for Cmax, Tmax, area under the curve (AUC) t, AUC inf, K el, half-life, and 90% confidence intervals applied for to check for whether reference and test products are equivalent. The experimental part of the study was completed with no major adversarial event. No losses or stern adverse events transpired throughout the course of the experiment. The assessment product is analogous to reference product in relation to degree and extent of absorption. The outcome of this study indicates the newly developed drug is equivalent to the innovator drug and medication was well tolerated by all participants.

Precision Clinical Medicine Through Machine Learning: Using High and Low Quantile Ranges of Vital Signs for Risk Stratification of ICU Patients

Remote monitoring of patients in the intensive care unit (ICU) is a crucial observation and assessment task that is necessary for precision medicine. In this work, we follow the state-of-the-art in risk stratification through machine learning-based prediction, but with minimal features that rely on vital signs, the most commonly used physiological variables obtained inside and outside hospitals. We provide a formal representation of a feature engineering algorithm and report the development and validation of three reproducible machine learning prediction models: ICU patient readmission, abnormality, and next-day vital sign measurements. For the readmission model, we proposed two solutions for data with imbalanced classes and applied five binary classification algorithms to each solution. For the abnormality model, we applied the same five algorithms to predict whether a patient will show abnormal health conditions. Our findings indicate that we can still achieve a reasonable performance using these machine learning models by focusing on low and high quantile ranges of vital signs. The best accuracy achieved in the readmission model was around 67.53%, with an area under the receiver operating characteristic (AUROC) of 0.7376. The highest accuracy achieved in the abnormality model was around 67.40%, with an AUROC of 0.7379. For the next-day vital sign measurements model, we provide three approaches for selecting model predictors and apply the eXtreme Gradient Boosting (XGB) and Random Forest Regression (RFR) algorithms to each solution. We found that, in general, the use of the most recent vital sign measurements achieves the least prediction error. Considering the large investment from the medical industry in patient monitoring devices, the developed models will be incorporated into an Intelligent ICU Patient Monitoring (IICUPM) module that can potentially facilitate the delivery of high quality care by implementing cost-efficient policies for handling the patients who utilize ICU resources the most.

Multi‐disciplinary collaborative consensus guidance statement on the assessment and treatment of breathing discomfort and respiratory sequelae in patients with post‐acute sequelae of SARS‐CoV‐2 infection (PASC)

<scp>Multi‐disciplinary</scp> collaborative consensus guidance statement on the assessment and treatment of breathing discomfort and respiratory sequelae in patients with <scp>post‐acute</scp> sequelae of <scp>SARS‐CoV</scp>‐2 infection (<scp>PASC</scp>)

Learning-Refined Integral Null Space Pursuit Algorithm for Noncontact Multisubjects Vital Signs Measurements Using SFCW-UWB and IR-UWB Radar

Under low signal-to-noise-and-clutter ratio circumstances, non-contact multi-subjects vital sign assessment based on ultrawideband (UWB) radar is extremely difficult. It is challenging to obtain an accurate measurement since the respiratory frequency harmonics are mixed with noise during detection and are extremely near to the frequency of the heartbeat signal. We use an impulse radio ultrawideband (IR-UWB) radar and a stepped-frequency continuous-wave ultrawideband (SFCW-UWB) radar in measurement to get reliable vital signs from several participants. For adaptively extracting respiratory and heartbeat patterns, it is proposed to use the learning refined integral null space pursuit algorithm (LR-INSP), which is based on the learning refined integral null space pursuit method and optimum route learning. The refined-INSP technique aims to provide fine-grained multi-layer and multi-branch harmonic components. Then, to obtain a reliable and stable vital sign path from the multi-branch decomposition, an optimum path learning technique is presented. According to the experimental findings, the average signal accuracy assessment (SAA) of the heartbeat and respiratory frequencies for IR-UWB radar is 95.41% and 89.97%, respectively. The average SAA of the heartbeat and respiratory frequencies for SFCW-UWB radar is 94.39% and 99.48%, respectively.

Mobile Robotic Platform for Contactless Vital Sign Monitoring

The COVID-19 pandemic has accelerated methods to facilitate contactless evaluation of patients in hospital settings. By minimizing in-person contact with individuals who may have COVID-19, healthcare workers can prevent disease transmission and conserve personal protective equipment. Obtaining vital signs is a ubiquitous task that is commonly done in person by healthcare workers. To eliminate the need for in-person contact for vital sign measurement in the hospital setting, we developed Dr. Spot, a mobile quadruped robotic system. The system includes IR and RGB cameras for vital sign monitoring and a tablet computer for face-to-face medical interviewing. Dr. Spot is teleoperated by trained clinical staff to simultaneously measure the skin temperature, respiratory rate, and heart rate while maintaining social distancing from patients and without removing their mask. To enable accurate, contactless measurements on a mobile system without a static black body as reference, we propose novel methods for skin temperature compensation and respiratory rate measurement at various distances between the subject and the cameras, up to 5 m. Without compensation, the skin temperature MAE is 1.3°C. Using the proposed compensation method, the skin temperature MAE is reduced to 0.3°C. The respiratory rate method can provide continuous monitoring with a MAE of 1.6 BPM in 30 s or rapid screening with a MAE of 2.1 BPM in 10 s. For the heart rate estimation, our system is able to achieve a MAE less than 8 BPM in 10 s measured in arbitrary indoor light conditions at any distance below 2 m.

Students’ Learning in the Pediatric Nursing Simulation “a Case Study of Vital Signs Measurement” Linked with Nursing Process

Students’ Learning in the Pediatric Nursing Simulation “a Case Study of Vital Signs Measurement” Linked with Nursing Process