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Sensors (Basel) ; 22(7)2022 Apr 05.
Article in English | MEDLINE | ID: covidwho-1785898

ABSTRACT

Continuous positive airway pressure (CPAP) telemonitoring (TMg) has become widely implemented in routine clinical care. Objective measures of CPAP compliance, residual respiratory events, and leaks can be easily monitored, but limitations exist. This review aims to assess the role of TMg in CPAP-treated obstructive sleep apnea (OSA) patients. We report recent data related to the accuracy of parameters measured by CPAP and try to determine the role of TMg in CPAP treatment follow-up, from the perspective of both healthcare professionals and patients. Measurement and accuracy of CPAP-recorded data, clinical management of these data, and impacts of TMg on therapy are reviewed in light of the current literature. Moreover, the crucial questions of who and how to monitor are discussed. TMg is a useful tool to support, fine-tune, adapt, and control both CPAP efficacy and compliance in newly-diagnosed OSA patients. However, clinicians should be aware of the limits of the accuracy of CPAP devices to measure residual respiratory events and leaks and issues such as privacy and cost-effectiveness are still a matter of concern. The best methods to focus our efforts on the patients who need TMg support should be properly defined in future long-term studies.


Subject(s)
Continuous Positive Airway Pressure , Sleep Apnea, Obstructive , Continuous Positive Airway Pressure/methods , Follow-Up Studies , Humans , Monitoring, Physiologic/methods , Patient Compliance , Sleep Apnea, Obstructive/therapy
4.
Sensors (Basel) ; 22(7)2022 Apr 02.
Article in English | MEDLINE | ID: covidwho-1785897

ABSTRACT

Sensors that track physiological biomarkers of health must be successfully incorporated into a fieldable, wearable device if they are to revolutionize the management of remote patient care and preventative medicine. This perspective article discusses logistical considerations that may impede the process of adapting a body-worn laboratory sensor into a commercial-integrated health monitoring system with a focus on examples from sleep tracking technology.


Subject(s)
Wearable Electronic Devices , Arrhythmias, Cardiac , Electrocardiography , Humans , Monitoring, Physiologic , Sleep
5.
Sensors (Basel) ; 22(7)2022 Mar 25.
Article in English | MEDLINE | ID: covidwho-1785895

ABSTRACT

Heart rate (HR) and respiratory rate (fR) can be estimated by processing videos framing the upper body and face regions without any physical contact with the subject. This paper proposed a technique for continuously monitoring HR and fR via a multi-ROI approach based on the spectral analysis of RGB video frames recorded with a mobile device (i.e., a smartphone's camera). The respiratory signal was estimated by the motion of the chest, whereas the cardiac signal was retrieved from the pulsatile activity at the level of right and left cheeks and forehead. Videos were recorded from 18 healthy volunteers in four sessions with different user-camera distances (i.e., 0.5 m and 1.0 m) and illumination conditions (i.e., natural and artificial light). For HR estimation, three approaches were investigated based on single or multi-ROI approaches. A commercially available multiparametric device was used to record reference respiratory signals and electrocardiogram (ECG). The results demonstrated that the multi-ROI approach outperforms the single-ROI approach providing temporal trends of both the vital parameters comparable to those provided by the reference, with a mean absolute error (MAE) consistently below 1 breaths·min-1 for fR in all the scenarios, and a MAE between 0.7 bpm and 6 bpm for HR estimation, whose values increase at higher distances.


Subject(s)
Electrocardiography , Respiratory Rate , Computers, Handheld , Heart Rate , Humans , Monitoring, Physiologic , Respiratory Rate/physiology , Signal Processing, Computer-Assisted
6.
Sensors (Basel) ; 22(7)2022 Mar 25.
Article in English | MEDLINE | ID: covidwho-1785894

ABSTRACT

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (1) Estimating occupancy and human activity phenotyping; (2) Medical equipment alarm classification; (3) Geolocation of humans in a built environment; (4) Ambient light logging; and (5) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.


Subject(s)
Privacy , Computers , Humans , Monitoring, Physiologic
7.
Front Public Health ; 9: 745524, 2021.
Article in English | MEDLINE | ID: covidwho-1775916

ABSTRACT

This paper presents an OSA patient interactive monitoring system based on the Beidou system. This system allows OSA patients to get timely rescue when they become sleepy outside. Because the Beidou position marker has an interactive function, it can reduce the anxiety of the patient while waiting for the rescue. At the same time, if a friend helps the OSA patients to call the doctor, the friend can also report the patient's condition in time. This system uses the popular IoT framework. At the bottom is the data acquisition layer, which uses wearable sensors to collect vital signs from patients, with a focus on ECG and SpO2 signals. The middle layer is the network layer that transmits the collected physiological signals to the Beidou indicator using the Bluetooth Low Energy (BLE) protocol. The top layer is the application layer, and the application layer uses the mature rescue interactive platform of Beidou. The Beidou system was developed by China itself, the main coverage of the satellite is in Asia, and is equipped with a high-density ground-based augmentation system. Therefore, the Beidou model improves the positioning accuracy and is equipped with a special communication satellite, which increases the short message interaction function. Therefore, patients can report disease progression in time while waiting for a rescue. After our simulation test, the effectiveness of the OSA patient rescue monitoring system based on the Beidou system and the positioning accuracy of OSA patients have been greatly improved. Especially when OSA patients work outdoors, the cell phone base station signal coverage is relatively weak. The satellite signal is well-covered, plus the SMS function of the Beidou indicator. Therefore, the system can be used to provide timely patient progress and provide data support for the medical rescue team to provide a more accurate rescue plan. After a comparative trial, the rescue rate of OSA patients using the detection device of this system was increased by 15 percentage points compared with the rescue rate using only GPS satellite phones.


Subject(s)
Cell Phone , Sleep Apnea, Obstructive , China , Humans , Monitoring, Physiologic , Sleep Apnea, Obstructive/diagnosis
8.
Front Immunol ; 13: 838985, 2022.
Article in English | MEDLINE | ID: covidwho-1742221

ABSTRACT

Introduction: Studies have shown reduced antiviral responses in kidney transplant recipients (KTRs) following SARS-CoV-2 mRNA vaccination, but data on post-vaccination alloimmune responses and antiviral responses against the Delta (B.1.617.2) variant are limited. Materials and methods: To address this issue, we conducted a prospective, multi-center study of 58 adult KTRs receiving mRNA-BNT162b2 or mRNA-1273 vaccines. We used multiple complementary non-invasive biomarkers for rejection monitoring including serum creatinine, proteinuria, donor-derived cell-free DNA, peripheral blood gene expression profile (PBGEP), urinary CXCL9 mRNA and de novo donor-specific antibodies (DSA). Secondary outcomes included development of anti-viral immune responses against the wild-type and Delta variant of SARS-CoV-2. Results: At a median of 85 days, no KTRs developed de novo DSAs and only one patient developed acute rejection following recent conversion to belatacept, which was associated with increased creatinine and urinary CXCL9 levels. During follow-up, there were no significant changes in proteinuria, donor-derived cell-free DNA levels or PBGEP. 36% of KTRs in our cohort developed anti-wild-type spike antibodies, 75% and 55% of whom had neutralizing responses against wild-type and Delta variants respectively. A cellular response against wild-type S1, measured by interferon-γ-ELISpot assay, developed in 38% of KTRs. Cellular responses did not differ in KTRs with or without antibody responses. Conclusions: SARS-CoV-2 mRNA vaccination in KTRs did not elicit a significant alloimmune response. About half of KTRs who develop anti-wild-type spike antibodies after two mRNA vaccine doses have neutralizing responses against the Delta variant. There was no association between anti-viral humoral and cellular responses.


Subject(s)
/immunology , Graft Rejection/diagnosis , Kidney Transplantation , Monitoring, Physiologic/methods , SARS-CoV-2/immunology , Aged , Antibodies, Viral/blood , Enzyme-Linked Immunospot Assay , Female , Humans , Immunity, Cellular , Isoantibodies/blood , Male , Middle Aged , Prospective Studies , Transplantation, Homologous , Vaccination
10.
Sci Rep ; 12(1): 3797, 2022 03 08.
Article in English | MEDLINE | ID: covidwho-1735276

ABSTRACT

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.


Subject(s)
Algorithms , COVID-19/diagnosis , Monitoring, Physiologic/methods , Area Under Curve , COVID-19/virology , Humans , Military Personnel , Monitoring, Physiologic/instrumentation , ROC Curve , SARS-CoV-2/isolation & purification , User-Computer Interface , Wearable Electronic Devices
11.
Sensors (Basel) ; 22(4)2022 Feb 13.
Article in English | MEDLINE | ID: covidwho-1715639

ABSTRACT

The article describes the implementation of IoT technology in the teaching of microprocessor technology. The method presented in the article combines the reality and virtualization of the microprocessor technology laboratory. A created IoT monitoring device monitors the students' microcontroller pins and sends the data to the server to which the teacher is connected via the control application. The teacher has the opportunity to monitor the development of tasks and student code of the program, where the functionality of these tasks can be verified. Thanks to the IoT remote laboratory implementation, students' tasks during the lesson were improved. As many as 53% (n = 8) of those students who could improve their results achieved an improvement of one or up to two tasks during class. Before the IoT remote laboratory application, up to 30% (n = 6) of students could not solve any task and only 25% (n = 5) solved two tasks (full number of tasks) during the class. Before implementation, 45% (n = 9) solved one problem. After applying the IoT remote laboratory, these numbers increased significantly and up to 50% (n = 10) of students solved the full number of tasks. In contrast, only 10% (n = 2) of students did not solve any task.


Subject(s)
Laboratories , Students , Humans , Monitoring, Physiologic
12.
Sensors (Basel) ; 22(3)2022 Feb 05.
Article in English | MEDLINE | ID: covidwho-1686945

ABSTRACT

Childhood obesity causes not only medical and psychosocial problems, it also reduces the life expectancy of the adults that they will become. On a large scale, obese adults adversely affect labor markets and the gross domestic product of countries. Monitoring the growth charts of children helps to maintain their body weight within healthy parameters according to the World Health Organization. Modern technologies allow the use of telehealth to carry out weight control programs and monitoring to verify children's compliance with the daily recommendations for risk factors that can be promoters of obesity, such as insufficient physical activity and insufficient sleep hours. In this work, we propose a secure remote monitoring and supervision scheme of physical activity and sleep hours for the children based on telehealth, multi-user networks, chaotic encryption, and spread spectrum, which, to our knowledge, is the first attempt to consider this service for safe pediatric telemedicine. In experimental results, we adapted a recent encryption algorithm in the literature for the proposed monitoring scheme using the assessment of childhood obesity as an application case in a multi-user network to securely send and receive fictitious parameters on childhood obesity of five users through the Internet by using just one communication channel. The results show that all the monitored parameters can be transmitted securely, achieving high sensitivity against secret key, enough secret key space, high resistance against noise interference, and 4.99 Mb/sec in computational simulations. The proposed scheme can be used to monitor childhood obesity in secure telehealth application.


Subject(s)
Pediatric Obesity , Telemedicine , Adult , Algorithms , Child , Exercise , Humans , Monitoring, Physiologic , Pediatric Obesity/prevention & control
13.
Sensors (Basel) ; 22(3)2022 Jan 27.
Article in English | MEDLINE | ID: covidwho-1686942

ABSTRACT

Wearable systems for monitoring biological signals have opened the door to personalized healthcare and have advanced a great deal over the past decade with the development of flexible electronics, efficient energy storage, wireless data transmission, and information processing technologies. As there are cumulative understanding of mechanisms underlying the mental processes and increasing desire for lifetime mental wellbeing, various wearable sensors have been devised to monitor the mental status from physiological activities, physical movements, and biochemical profiles in body fluids. This review summarizes the recent progress in wearable healthcare monitoring systems that can be utilized in mental healthcare, especially focusing on the biochemical sensors (i.e., biomarkers associated with mental status, sensing modalities, and device materials) and discussing their promises and challenges.


Subject(s)
Wearable Electronic Devices , Delivery of Health Care , Electronics , Mental Health , Monitoring, Physiologic
14.
Sensors (Basel) ; 22(3)2022 Feb 05.
Article in English | MEDLINE | ID: covidwho-1674772

ABSTRACT

Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient's health condition remotely. Every layer has specific functionality in the COVID-19 symptoms' monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis.


Subject(s)
COVID-19 , Wearable Electronic Devices , Delivery of Health Care , Humans , Monitoring, Physiologic , SARS-CoV-2
17.
Stud Health Technol Inform ; 289: 162-165, 2022 Jan 14.
Article in English | MEDLINE | ID: covidwho-1643438

ABSTRACT

Due to the COVID-19 pandemic, home-office has turned to be a common practice in many companies to limit physical contact to reduce the rate of infections in the workplace. To quantify office workers' ADLs, this work demonstrates unobtrusive monitoring of activities of daily living (ADLs) of an office worker in a home-office environment with three low-cost sensors: an accelerometer and two light sensors. We extract four elementary events: distinct and fain chair movement, monitor, and fridge usage, from which we derived seven ADLs using predefined rules. This simple system can support the quantification of ADLs of home-office workers.


Subject(s)
Activities of Daily Living , COVID-19 , Humans , Monitoring, Physiologic , Pandemics , SARS-CoV-2
18.
PLoS One ; 17(1): e0261523, 2022.
Article in English | MEDLINE | ID: covidwho-1643245

ABSTRACT

BACKGROUND: The COVID-19 epidemic in Italy has severely affected people aged more than 80, especially socially isolated. Aim of this paper is to assess whether a social and health program reduced mortality associated to the epidemic. METHODS: An observational retrospective cohort analysis of deaths recorded among >80 years in three Italian cities has been carried out to compare death rate of the general population and "Long Live the Elderly!" (LLE) program. Parametric and non-parametric tests have been performed to assess differences of means between the two populations. A multivariable analysis to assess the impact of covariates on weekly mortality has been carried out by setting up a linear mixed model. RESULTS: The total number of services delivered to the LLE population (including phone calls and home visits) was 34,528, 1 every 20 day per person on average, one every 15 days during March and April. From January to April 2019, the same population received one service every 41 days on average, without differences between January-February and March-April. The January-April 2020 cumulative crude death rate was 34.8‰ (9,718 deaths out of 279,249 individuals; CI95%: 34.1-35.5) and 28.9‰ (166 deaths out of 5,727 individuals; CI95%:24.7-33.7) for the general population and the LLE sample respectively. The general population weekly death rate increased after the 11th calendar week that was not the case among the LLE program participants (p<0.001). The Standardized Mortality Ratio was 0.83; (CI95%: 0.71-0.97). Mortality adjusted for age, gender, COVID-19 weekly incidence and prevalence of people living in nursing homes was lower in the LLE program than in the general population (p<0.001). CONCLUSIONS: LLE program is likely to limit mortality associated with COVID-19. Further studies are needed to establish whether it is due to the impact of social care that allows a better clients' adherence to the recommendations of physical distancing or to an improved surveillance of older adults that prevents negative outcomes associated with COVID-19.


Subject(s)
COVID-19/epidemiology , Community Health Services/organization & administration , Homes for the Aged/organization & administration , Monitoring, Physiologic/methods , Nursing Homes/organization & administration , SARS-CoV-2/pathogenicity , Aged, 80 and over , COVID-19/mortality , COVID-19/psychology , Cities , Community Health Services/ethics , Female , Homes for the Aged/ethics , Humans , Incidence , Italy/epidemiology , Male , Nursing Homes/ethics , Physical Distancing , Retrospective Studies , Social Isolation/psychology , Survival Analysis
19.
Sensors (Basel) ; 22(2)2022 Jan 14.
Article in English | MEDLINE | ID: covidwho-1639569

ABSTRACT

Home-based healthcare provides a viable and cost-effective method of delivery for resource- and labour-intensive therapies, such as rehabilitation therapies, including anorectal biofeedback. However, existing systems for home anorectal biofeedback are not able to monitor patient compliance or assess the quality of exercises performed, and as a result have yet to see wide spread clinical adoption. In this paper, we propose a new Internet of Medical Things (IoMT) system to provide home-based biofeedback therapy, facilitating remote monitoring by the physician. We discuss our user-centric design process and the proposed architecture, including a new sensing probe, mobile app, and cloud-based web application. A case study involving biofeedback training exercises was performed. Data from the IoMT was compared against the clinical standard, high-definition anorectal manometry. We demonstrated the feasibility of our proposed IoMT in providing anorectal pressure profiles equivalent to clinical manometry and its application for home-based anorectal biofeedback therapy.


Subject(s)
Internet of Things , Rectal Diseases , Biofeedback, Psychology , Humans , Internet , Manometry , Monitoring, Physiologic
20.
Sensors (Basel) ; 22(2)2022 Jan 14.
Article in English | MEDLINE | ID: covidwho-1625927

ABSTRACT

In this study, a contactless vital signs monitoring system was proposed, which can measure body temperature (BT), heart rate (HR) and respiration rate (RR) for people with and without face masks using a thermal and an RGB camera. The convolution neural network (CNN) based face detector was applied and three regions of interest (ROIs) were located based on facial landmarks for vital sign estimation. Ten healthy subjects from a variety of ethnic backgrounds with skin colors from pale white to darker brown participated in several different experiments. The absolute error (AE) between the estimated HR using the proposed method and the reference HR from all experiments is 2.70±2.28 beats/min (mean ± std), and the AE between the estimated RR and the reference RR from all experiments is 1.47±1.33 breaths/min (mean ± std) at a distance of 0.6-1.2 m.


Subject(s)
COVID-19 , Algorithms , Body Temperature , Heart Rate , Humans , Monitoring, Physiologic , Respiratory Rate , SARS-CoV-2 , Vital Signs
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