A Home Telemedicine System for Continuous Respiratory Monitoring

Detrended fluctuation analysis of day and night breathing parameters from a wearable respiratory holter

BACKGROUND AND OBJECTIVE

This study focuses on the application of Detrended Fluctuation Analysis (DFA) to understand the variability and correlation properties of respiratory parameters time series obtained by means of a wearable.

METHODS

Data from 18 healthy volunteers collected using the Airgo™ band, which provides signals proportional to thoracic circumference at a sampling frequency of 10 Hz. The primary aim was to provide preliminary normative data for DFA scaling factors.

RESULTS

DFA was applied to 6-h recordings, revealing significant differences (p < 0.001) in scaling factors (α values) for tidal volume (night: 0.97 [0.09], day: 0.88 [0.04]), minute ventilation (night: 1.02 [0.10], day: 0.91 [0.07), mean inspiratory flow (night: 0.98 [0.06], day: 0.88 [0.06]), mean expiratory flow (night: 0.89 [0.08], day: 0.81 [0.06]), and duty cycle (night: 0.64 [0.04], day: 0.59 [0.03]). Quadratic detrending highlighted additional differences not captured with linear detrending, particularly in inspiratory and expiratory time. These findings suggest distinct regulatory patterns during sleep.

CONCLUSIONS

DFA analysis of respiratory parameters obtained from wearable devices reveals distinct regulatory patterns between day and night conditions, particularly in parameters related to tidal volume and ventilation. These findings demonstrate the potential of DFA to uncover physiological differences in respiratory control mechanisms, especially during sleep, despite technical limitations such as the strong dependency of DFA scaling factors on sampling frequency, duration, and detrending order. Future research should address the limitations of sample size and expand normative datasets to include individuals with respiratory conditions, to translate this methodology into specific clinical applications.

TinyML and edge intelligence applications in cardiovascular disease: A survey

Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated at the extreme edge of networks. In this paper, we explore the transformative potential of TinyML in facilitating pervasive, low-power cardiovascular monitoring and real-time analytics for patients with cardiac anomalies, leveraging wearable devices as the primary interface. To begin with, we provide an overview of TinyML software and hardware enablers, accompanied by an examination of networking solutions such as Low-power Wide area network (LPWAN) that facilitate the seamless deployment of TinyML frameworks. Following this, we delve into the methodologies of knowledge distillation, quantization, and pruning, which represent the cornerstone strategies for optimizing machine learning models to operate efficiently within resource-constrained environments. Furthermore, our discussion extends to the role of efficient deep neural networks tailored specifically for cardiovascular monitoring on wearable devices with limited computational resources. Through a comprehensive review, we analyze the applications of prominent artificial neural network architectures including Convolutional Neural Networks (CNNs), Autoencoders, Deep Belief Networks (DBNs), and Transformers in the domain of Electrocardiogram (ECG) analytics, shedding light on their efficacy and potential in advancing healthcare technology.

Wearable devices for patient monitoring in the intensive care unit

Wearable devices (WDs), originally launched for fitness, are now increasingly recognized as valuable technologies in several clinical applications, including the intensive care unit (ICU). These devices allow for continuous, non-invasive monitoring of physiological parameters such as heart rate, respiratory rate, blood pressure, glucose levels, and posture and movement. WDs offer significant advantages in making monitoring less invasive and could help bridge gaps between ICUs and standard hospital wards, ensuring more effective transitioning to lower-level monitoring after discharge from the ICU. WDs are also promising tools in applications like delirium detection, vital signs monitoring in limited resource settings, and prevention of hospital-acquired pressure injuries. Despite the potential of WDs, challenges such as measurement accuracy, explainability of data processing algorithms, and actual integration into the clinical decision-making process persist. Further research is necessary to validate the effectiveness of WDs and to integrate them into clinical practice in critical care environments.Take home messages Wearable devices are revolutionizing patient monitoring in ICUs and step down units by providing continuous, non-invasive, and cost-effective solutions. Validation of their accuracy and integration in the clinical decision-making process remain crucial for widespread clinical adoption.

5G-enabled smart hospitals: Innovations in patient care and facility management

Smart hospitals represent the pinnacle of healthcare innovation, leveraging cutting-edge technologies to transform patient care and facility management. This article addresses the pressing need for effective implementation of 5G technology in smart hospitals, aiming to enhance connectivity, improve patient outcomes, and drive operational efficiency. The methodology employed involves a comprehensive review of existing literature, case studies, and expert insights to analyze the impact of 5G on various aspects of smart hospital operations. The article highlights the significance of 5G technology in enabling real-time data analytics, remote monitoring, and telemedicine, thus revolutionizing healthcare delivery. By providing high-speed, low-latency connectivity, 5G facilitates seamless communication and collaboration among healthcare providers, leading to more efficient diagnosis, treatment, and patient care. Additionally, the adoption of 5G enables smart hospitals to leverage artificial intelligence (AI)-based solutions for predictive analytics, personalized medicine and enhanced patient engagement. Furthermore, the article explores the potential of 5G-enabled smart hospitals in enhancing disaster preparedness and emergency response efforts. Case studies and examples demonstrate how 5G technology can improve situational awareness, coordinate resources, and deliver timely care during natural disasters and pandemics. Overall, this article underscores the transformative impact of 5G technology on smart hospitals and emphasizes the importance of embracing innovation to meet the evolving needs of patients and communities. By adopting 5G technology, smart hospitals can usher in a new era of healthcare delivery characterized by enhanced connectivity, improved patient outcomes, and unparalleled efficiency.

Development of a real-time RGB-D visual feedback-assisted pulmonary rehabilitation system

Background

Following surgery, perioperative pulmonary rehabilitation (PR) is important for patients with early-stage lung cancer. However, current inpatient programs are often limited in time and space, and outpatient settings have access barriers. Therefore, we aimed to develop a background-free, zero-contact thoracoabdominal movement-tracking model that is easily set up and incorporated into a pre-existing PR program or extended to home-based rehabilitation and remote monitoring. We validated its effectiveness in providing preclinical real-time RGB-D (colour-depth camera) visual feedback.

Methods

Twelve healthy volunteers performed deep breathing exercises following audio instruction for three cycles, followed by audio instruction and real-time visual feedback for another three cycles. In the visual feedback system, we used a RealSense™ D415 camera to capture RGB and depth images for human pose-estimation with Google MediaPipe. Target-tracking regions were defined based on the relative position of detected joints. The processed depth information of the tracking regions was visualised on a screen as a motion bar to provide real-time visual feedback of breathing intensity. Pulmonary function was simultaneously recorded using spirometric measurements, and changes in pulmonary volume were derived from respiratory airflow signals.

Results

Our movement-tracking model showed a very strong correlation (r = 0.90 ± 0.05) between thoracic motion signals and spirometric volume, and a strong correlation (r = 0.73 ± 0.22) between abdominal signals and spirometric volume. Displacement of the chest wall was enhanced by RGB-D visual feedback (23 vs 20 mm, P = 0.034), and accompanied by an increased lung volume (2.58 vs 2.30 L, P = 0.003).

Conclusion

We developed an easily implemented thoracoabdominal movement-tracking model and reported the positive impact of real-time RGB-D visual feedback on self-promoted external chest wall expansion, accompanied by increased internal lung volumes. This system can be extended to home-based PR.

A Biobanking System for Diagnostic Images: Architecture Development, COVID-19-Related Use Cases, and Performance Evaluation

BACKGROUND

Systems capable of automating and enhancing the management of research and clinical data represent a significant contribution of information and communication technologies to health care. A recent advancement is the development of imaging biobanks, which are now enabling the collection and storage of diagnostic images, clinical reports, and demographic data to allow researchers identify associations between lifestyle and genetic factors and imaging-derived phenotypes.

OBJECTIVE

The aim of this study was to design and evaluate the system performance of a network for an operating biobank of diagnostic images, the Bio Check Up Srl (BCU) Imaging Biobank, based on the Extensible Neuroimaging Archive Toolkit open-source platform.

METHODS

Three usage cases were designed focusing on evaluation of the memory and computing consumption during imaging collections upload and during interactions between two kinds of users (researchers and radiologists) who inspect chest computed tomography scans of a COVID-19 cohort. The experiments considered three network setups: (1) a local area network, (2) virtual private network, and (3) wide area network. The experimental setup recorded the activity of a human user interacting with the biobank system, which was continuously replayed multiple times. Several metrics were extracted from network traffic traces and server logs captured during the activity replay.

RESULTS

Regarding the diagnostic data transfer, two types of containers were considered: the Web and the Database containers. The Web appeared to be the more memory-hungry container with a higher computational load (average 2.7 GB of RAM) compared to that of the database. With respect to user access, both users demonstrated the same network performance level, although higher resource consumption was registered for two different actions: DOWNLOAD & LOGOUT (100%) for the researcher and OPEN VIEWER (20%-50%) for the radiologist.

CONCLUSIONS

This analysis shows that the current setup of BCU Imaging Biobank is well provisioned for satisfying the planned number of concurrent users. More importantly, this study further highlights and quantifies the resource demands of specific user actions, providing a guideline for planning, setting up, and using an image biobanking system.

Digital technologies for step counting: between promises of reliability and risks of reductionism

Step counting is among the fundamental features of wearable technology, as it grounds several uses of wearables in biomedical research and clinical care, is at the center of emerging public health interventions and recommendations, and is gaining increasing scientific and political importance. This paper provides a perspective of step counting in wearable technology, identifying some limitations to the ways in which wearable technology measures steps and indicating caution in current uses of step counting as a proxy for physical activity. Based on an overview of the current state of the art of technologies and approaches to step counting in digital wearable technologies, we discuss limitations that are methodological as well as epistemic and ethical-limitations to the use of step counting as a basis to build scientific knowledge on physical activity (epistemic limitations) as well as limitations to the accessibility and representativity of these tools (ethical limitations). As such, using step counting as a proxy for physical activity should be considered a form of reductionism. This is not per se problematic, but there is a need for critical appreciation and awareness of the limitations of reductionistic approaches. Perspective research should focus on holistic approaches for better representation of physical activity levels and inclusivity of different user populations.

Designing a Clinician-Centered Wearable Data Dashboard (CarePortal): Participatory Design Study

BACKGROUND

The recent growth of eHealth is unprecedented, especially after the COVID-19 pandemic. Within eHealth, wearable technology is increasingly being adopted because it can offer the remote monitoring of chronic and acute conditions in daily life environments. Wearable technology may be used to monitor and track key indicators of physical and psychological stress in daily life settings, providing helpful information for clinicians. One of the key challenges is to present extensive wearable data to clinicians in an easily interpretable manner to make informed decisions.

OBJECTIVE

The purpose of this research was to design a wearable data dashboard, named CarePortal, to present analytic visualizations of wearable data that are meaningful to clinicians. The study was divided into 2 main research objectives: to understand the needs of clinicians regarding wearable data interpretation and visualization and to develop a system architecture for a web application to visualize wearable data and related analytics.

METHODS

We used a wearable data set collected from 116 adolescent participants who experienced trauma. For 2 weeks, participants wore a Microsoft Band that logged physiological sensor data such as heart rate (HR). A total of 834 days of HR data were collected. To design the CarePortal dashboard, we used a participatory design approach that interacted directly with clinicians (stakeholders) with backgrounds in clinical psychology and neuropsychology. A total of 8 clinicians were recruited from the Rhode Island Hospital and the University of Massachusetts Memorial Health. The study involved 5 stages of participatory workshops and began with an understanding of the needs of clinicians. A User Experience Questionnaire was used at the end of the study to quantitatively evaluate user experience. Physiological metrics such as daily and hourly maximum, minimum, average, and SD of HR and HR variability, along with HR-based activity levels, were identified. This study investigated various data visualization graphing methods for wearable data, including radar charts, stacked bar plots, scatter plots combined with line plots, simple bar plots, and box plots.

RESULTS

We created a CarePortal dashboard after understanding the clinicians' needs. Results from our workshops indicate that overall clinicians preferred aggregate information such as daily HR instead of continuous HR and want to see trends in wearable sensor data over a period (eg, days). In the User Experience Questionnaire, a score of 1.4 was received, which indicated that CarePortal was exciting to use (question 5), and a similar score was received, indicating that CarePortal was the leading edge (question 8). On average, clinicians reported that CarePortal was supportive and can be useful in making informed decisions.

CONCLUSIONS

We concluded that the CarePortal dashboard integrated with wearable sensor data visualization techniques would be an acceptable tool for clinicians to use in the future.

A long distance telesurgical demonstration on robotic surgery phantoms over 5G

PURPOSE

Using robotic technology and communications infrastructure to remotely perform surgery has been a persistent goal in medical research in the past three decades. The recent deployment of the Fifth-Generation Wireless Networks has revitalized the research efforts in the telesurgery paradigm. Offering low latency and high bandwidth communication, they are well suited for applications that require real-time data transmission and can allow smoother communication between surgeon and patient, making it possible to remotely perform complex surgeries. In this paper, we investigate the effects of the 5 G network on surgical performance during a telesurgical demonstration where the surgeon and the robot are separated by nearly 300 km.

METHODS

The surgeon performed surgical exercises on a robotic surgery training phantom using a novel telesurgical platform. The master controllers were connected to the local site on a 5 G network, teleoperating the robot remotely in a hospital. A video feed of the remote site was also streamed. The surgeon performed various tasks on the phantom such as cutting, dissection, pick-and-place and ring tower transfer. To assess the usefulness, usability and image quality of the system, the surgeon was subsequently interviewed using three structured questionnaires.

RESULTS

All tasks were completed successfully. The low latency and high bandwidth of the network resulted into a latency of 18 ms for the motion commands while the video delay was about 350 ms. This enabled the surgeon to operate smoothly with a high-definition video from about 300 km away. The surgeon viewed the system's usability in a neutral to positive way while the video image was rated as of good quality.

CONCLUSION

5 G networks provide significant advancement in the field of telecommunications, offering faster speeds and lower latency than previous generations of wireless technology. They can serve as an enabling technology for telesurgery and further advance its application and adoption.

An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions

PURPOSE

Breathing parameters change with activity and posture, but currently available solutions can perform measurements only during static conditions.

METHODS

This article presents an innovative wearable sensor system constituted by three inertial measurement units to simultaneously estimate respiratory rate (RR) in static and dynamic conditions and perform human activity recognition (HAR) with the same sensing principle. Two units are aimed at detecting chest wall breathing-related movements (one on the thorax, one on the abdomen); the third is on the lower back. All units compute the quaternions describing the subject's movement and send data continuously with the ANT transmission protocol to an app. The 20 healthy subjects involved in the research (9 men, 11 women) were between 23 and 54 years old, with mean age 26.8, mean height 172.5 cm and mean weight 66.9 kg. Data from these subjects during different postures or activities were collected and analyzed to extract RR.

RESULTS

Statistically significant differences between dynamic activities ("walking slow", "walking fast", "running" and "cycling") and static postures were detected (p < 0.05), confirming the obtained measurements are in line with physiology even during dynamic activities. Data from the reference unit only and from all three units were used as inputs to artificial intelligence methods for HAR. When the data from the reference unit were used, the Gated Recurrent Unit was the best performing method (97% accuracy). With three units, a 1D Convolutional Neural Network was the best performing (99% accuracy).

CONCLUSION

Overall, the proposed solution shows it is possible to perform simultaneous HAR and RR measurements in static and dynamic conditions with the same sensor system.

Fitbit Data to Assess Functional Capacity in Patients Before Elective Surgery: Pilot Prospective Observational Study

BACKGROUND

Preoperative assessment is crucial to prevent the risk of complications of surgical operations and is usually focused on functional capacity. The increasing availability of wearable devices (smartwatches, trackers, rings, etc) can provide less intrusive assessment methods, reduce costs, and improve accuracy.

OBJECTIVE

The aim of this study was to present and evaluate the possibility of using commercial smartwatch data, such as those retrieved from the Fitbit Inspire 2 device, to assess functional capacity before elective surgery and correlate such data with the current gold standard measure, the 6-Minute Walk Test (6MWT) distance.

METHODS

During the hospital visit, patients were evaluated in terms of functional capacity using the 6MWT. Patients were asked to wear the Fitbit Inspire 2 for 7 days (with flexibility of -2 to +2 days) after the hospital visit, before their surgical operation. Resting heart rate and daily steps data were retrieved directly from the smartwatch. Feature engineering techniques allowed the extraction of heart rate over steps (HROS) and a modified version of Non-Exercise Testing Cardiorespiratory Fitness. All measures were correlated with 6MWT.

RESULTS

In total, 31 patients were enrolled in the study (n=22, 71% men; n=9, 29% women; mean age 76.06, SD 4.75 years). Data were collected between June 2021 and May 2022. The parameter that correlated best with the 6MWT was the Non-Exercise Testing Cardiorespiratory Fitness index (r=0.68; P<.001). The average resting heart rate over the whole acquisition period for each participant had r=-0.39 (P=.03), even if some patients did not wear the device at night. The correlation of the 6MWT distance with the HROS evaluated at 1% quantile was significant, with Pearson coefficient of -0.39 (P=.04). Fitbit step count had a fair correlation of 0.59 with 6MWT (P<.001).

CONCLUSIONS

Our study is a promising starting point for the adoption of wearable technology in the evaluation of functional capacity of patients, which was strongly correlated with the gold standard. The study also identified limitations in the availability of metrics, variability of devices, accuracy and quality of data, and accessibility as crucial areas of focus for future studies.

Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications during the Pandemic

5G is being implemented in the Internet of things (IoT) era. This book chapter focuses on 5G technology and the integration of other digital technologies, such as artificial intelligence (AI) and machine learning, IoT, big data analytics, cloud computing, robotics, and other digital platforms into new healthcare applications. Now, the healthcare industry is implementing 5G-enabled technology to improve health services, medical research, quality of life, and medical professionals’ and patients’ experiences everywhere, at any time. Technology can facilitate faster medical research progress and better clinical and social services management. Furthermore, AI approaches with 5G connectivity may be able to combat the epidemic challenges with minimal resources. This book chapter underlines how 5G technology is growing to address epidemic concerns. The study highlights many technical issues and future developments for creating 5G-powered healthcare solutions. This chapter also addresses the key challenges AI and 5G technology face in emerging healthcare solutions. In addition, this book chapter highlights perspective, policy recommendations, and future research directions of AI and 5G-enabled technologies in confronting future pandemics. More research will be incorporated into future projects, including studies on developing a digital society based on 5G technology in healthcare emergencies.

The paradox of the artificial intelligence system development process: the use case of corporate wellness programs using smart wearables

Artificial intelligence (AI) systems have been widely applied to various contexts, including high-stake decision processes in healthcare, banking, and judicial systems. Some developed AI models fail to offer a fair output for specific minority groups, sparking comprehensive discussions about AI fairness. We argue that the development of AI systems is marked by a central paradox: the less participation one stakeholder has within the AI system's life cycle, the more influence they have over the way the system will function. This means that the impact on the fairness of the system is in the hands of those who are less impacted by it. However, most of the existing works ignore how different aspects of AI fairness are dynamically and adaptively affected by different stages of AI system development. To this end, we present a use case to discuss fairness in the development of corporate wellness programs using smart wearables and AI algorithms to analyze data. The four key stakeholders throughout this type of AI system development process are presented. These stakeholders are called service designer, algorithm designer, system deployer, and end-user. We identify three core aspects of AI fairness, namely, contextual fairness, model fairness, and device fairness. We propose a relative contribution of the four stakeholders to the three aspects of fairness. Furthermore, we propose the boundaries and interactions between the four roles, from which we make our conclusion about the possible unfairness in such an AI developing process.

A Scoping Review on Wearable Devices for Environmental Monitoring and Their Application for Health and Wellness

This scoping review is focused on wearable devices for environmental monitoring. First, the main pollutants are presented, followed by sensing technologies that are used for the parameters of interest. Selected examples of wearables and portables are divided into commercially available and research-level projects. While many commercial products are in fact portable, there is an increasing interest in using a completely wearable technology. This allows us to correlate the pollution level to other personal information (performed activity, position, and respiratory parameters) and thus to estimate personal exposure to given pollutants. The fact that there are no univocal indices to estimate outdoor or indoor air quality is also an open problem. Finally, applications of wearables for environmental monitoring are discussed. Combining environmental monitoring with other devices would permit better choices of where to perform sports activities, especially in highly polluted areas, and provide detailed information on the living conditions of individuals.

Magic of 5G Technology and Optimization Methods Applied to Biomedical Devices: A Survey

Wireless networks have gained significant attention and importance in healthcare as various medical devices such as mobile devices, sensors, and remote monitoring equipment must be connected to communication networks. In order to provide advanced medical treatments to patients, high-performance technologies such as the emerging fifth generation/sixth generation (5G/6G) are required for transferring data to and from medical devices and in addition to their major components developed with improved optimization methods which are substantially needed and embedded in them. Providing intelligent system design is a challenging task in medical applications, as it affects the whole behaviors of medical devices. A critical review of the medical devices and the various optimization methods employed are presented in this paper, to pave the way for designers to develop an apparatus that is applicable in the healthcare industry under 5G technology and future 6G wireless networks.

5G in Healthcare: from COVID-19 to Future Challenges

Worldwide up to May 2022 there have been 515 million cases of COVID-19 infection and over 6 million deaths. The World Health Organization estimated that 115,000 healthcare workers died from COVID-19 from January 2020 to May 2021. This toll on human lives prompted this review on 5G based networking primarily on major components of healthcare delivery: diagnosis, patient monitoring, contact tracing, diagnostic imaging tests, vaccines distribution, emergency medical services, telesurgery and robot-assisted tele-ultrasound. The positive impact of 5G as core technology for COVID-19 applications enabled exchange of huge data sets in fangcang (cabin) hospitals and real-time contact tracing, while the low latency enhanced robot-assisted tele-ultrasound, and telementoring during ophthalmic surgery. In other instances, 5G provided a supportive technology for applications related to COVID-19, e.g., patient monitoring. The feasibility of 5G telesurgery was proven, albeit by a few studies on real patients, in very low samples size in most instances. The important future applications of 5G in healthcare include surveillance of elderly people, the immunosuppressed, and nano- oncology for Internet of Nano Things (IoNT). Issues remain and these require resolution before routine clinical adoption. These include infrastructure and coverage; health risks; security and privacy protection of patients' data; 5G implementation with artificial intelligence, blockchain, and IoT; validation, patient acceptance and training of end-users on these technologies.

Communication Requirements in 5G-Enabled Healthcare Applications: Review and Considerations

Fifth generation (5G) mobile communication technology can enable novel healthcare applications and augment existing ones. However, 5G-enabled healthcare applications demand diverse technical requirements for radio communication. Knowledge of these requirements is important for developers, network providers, and regulatory authorities in the healthcare sector to facilitate safe and effective healthcare. In this paper, we review, identify, describe, and compare the requirements for communication key performance indicators in relevant healthcare use cases, including remote robotic-assisted surgery, connected ambulance, wearable and implantable devices, and service robotics for assisted living, with a focus on quantitative requirements. We also compare 5G-healthcare requirements with the current state of 5G capabilities. Finally, we identify gaps in the existing literature and highlight considerations for this space.

Computation of Gait Parameters in Post Stroke and Parkinson's Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems

The accurate and reliable assessment of gait parameters is assuming an important role, especially in the perspective of designing new therapeutic and rehabilitation strategies for the remote follow-up of people affected by disabling neurological diseases, including Parkinson’s disease and post-stroke injuries, in particular considering how gait represents a fundamental motor activity for the autonomy, domestic or otherwise, and the health of neurological patients. To this end, the study presents an easy-to-use and non-invasive solution, based on a single RGB-D sensor, to estimate specific features of gait patterns on a reduced walking path compatible with the available spaces in domestic settings. Traditional spatio-temporal parameters and features linked to dynamic instability during walking are estimated on a cohort of ten parkinsonian and eleven post-stroke subjects using a custom-written software that works on the result of a body-tracking algorithm. Then, they are compared with the “gold standard” 3D instrumented gait analysis system. The statistical analysis confirms no statistical difference between the two systems. Data also indicate that the RGB-D system is able to estimate features of gait patterns in pathological individuals and differences between them in line with other studies. Although they are preliminary, the results suggest that this solution could be clinically helpful in evolutionary disease monitoring, especially in domestic and unsupervised environments where traditional gait analysis is not usable.

Comparison between PtCO2 and PaCO2 and Derived Parameters in Heart Failure Patients during Exercise: A Preliminary Study

Evaluation of arterial carbon dioxide pressure (PaCO2) and dead space to tidal volume ratio (VD/VT) during exercise is important for the identification of exercise limitation causes in heart failure (HF). However, repeated sampling of arterial or arterialized ear lobe capillary blood may be clumsy. The aim of our study was to estimate PaCO2 by means of a non-invasive technique, transcutaneous PCO2 (PtCO2), and to verify the correlation between PtCO2 and PaCO2 and between their derived parameters, such as VD/VT, during exercise in HF patients. 29 cardiopulmonary exercise tests (CPET) performed on a bike with a ramp protocol aimed at achieving maximal effort in ≈10 min were analyzed. PaCO2 and PtCO2 values were collected at rest and every 2 min during active pedaling. The uncertainty of PCO2 and VD/VT measurements were determined by analyzing the error between the two methods. The accuracy of PtCO2 measurements vs. PaCO2 decreases towards the end of exercise. Therefore, a correction to PtCO2 that keeps into account the time of the measurement was implemented with a multiple regression model. PtCO2 and VD/VT changes at 6, 8 and 10 min vs. 2 min data were evaluated before and after PtCO2 correction. PtCO2 overestimates PaCO2 for high timestamps (median error 2.45, IQR -0.635-5.405, at 10 min vs. 2 min, p-value = 0.011), while the error is negligible after correction (median error 0.50, IQR = -2.21-3.19, p-value > 0.05). The correction allows removing differences also in PCO2 and VD/VT changes. In HF patients PtCO2 is a reliable PaCO2 estimation at rest and at low exercise intensity. At high exercise intensity the overall response appears delayed but reproducible and the error can be overcome by mathematical modeling allowing an accurate estimation by PtCO2 of PaCO2 and VD/VT.

5G Use in Healthcare: The Future is Present

Background:

Most healthcare providers are unaware of the extraordinary opportunities for implementation in healthcare which can be enabled by 5G wireless networks. 5G created enormous opportunities for a myriad of new technologies, resulting in an integrated through 5G ‘ecosystem’. Although the new opportunities in healthcare are immense, medicine is slow to change, as manifest by the paucity of new, innovative applications based upon this ecosystem. Thus, emerges the need to “avoid technology surprise” - both laparoscopic and robotic assisted minimally invasive surgery were delayed for years because the surgical community was either unaware or unaccepting of a new technology.

Database:

PubMed (Medline) and Scopus (Elsevier) databases were searched and all published studies regarding clinical applications of 5G were retrieved. From a total of 40 articles, 13 were finally included in our review.

Discussion:

The important transformational properties of 5G communications and other innovative technologies are described and compared to healthcare needs, looking for opportunities, limitations, and challenges to implementation of 5G and the ecosystem it has spawned. Furthermore, the needs in the clinical applications, education and research in medicine and surgery, in addition to the administrative infrastructure are addressed. Additionally, we explore the nontechnical challenges, that either support or oppose this new healthcare renovation. Based upon proven advantages of these innovative technologies, current scientific evidence is analyzed for future trends for the transformation of healthcare. By providing awareness of these opportunities and their advantages for patients, it will be possible to decrease the prolonged timeframe for acceptance and implementation for patients.

Assessing the Tidal Volume through Wearables: A Scoping Review

The assessment of respiratory activity based on wearable devices is becoming an area of growing interest due to the wide range of available sensors. Accordingly, this scoping review aims to identify research evidence supporting the use of wearable devices to monitor the tidal volume during both daily activities and clinical settings. A screening of the literature (Pubmed, Scopus, and Web of Science) was carried out in December 2020 to collect studies: i. comparing one or more methodological approaches for the assessment of tidal volume with the outcome of a state-of-the-art measurement device (i.e., spirometry or optoelectronic plethysmography); ii. dealing with technological solutions designed to be exploited in wearable devices. From the initial 1031 documents, only 36 citations met the eligibility criteria. These studies highlighted that the tidal volume can be estimated by using different technologies ranging from IMUs to strain sensors (e.g., resistive, capacitive, inductive, electromagnetic, and optical) or acoustic sensors. Noticeably, the relative volumetric error of these solutions during quasi-static tasks (e.g., resting and sitting) is typically ≥10% but it deteriorates during dynamic motor tasks (e.g., walking). As such, additional efforts are required to improve the performance of these devices and to identify possible applications based on their accuracy and reliability.

Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems

In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov's method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application.

Telemedicine in the management of patients with chronic respiratory failure

Patients with chronic respiratory failure are often required to attend multiple hospital appointments, which may be difficult due to their physical disabilities and the amount of equipment they are required to bring. Their caregivers often struggle with the lack of immediate care available when the patient suffers difficulties at home. Telemedicine is an opportunity to bridge the gap between home and healthcare professionals by allowing the healthcare team to reach into patients' homes to provide more frequent support. The evidence for the use of telemedicine in patients with chronic respiratory failure remains equivocal. Although the uptake of telemedicine has been slow, the SARS-CoV-2 pandemic has resulted in the rapid dissemination of telemedicine to allow the delivery of care to vulnerable patients while reducing the need for their attendance in hospital. Logistical and legal challenges to the delivery of telemedicine remain, but the pandemic may serve as a driver to ameliorate these challenges and facilitate wider use of this technology to improve the experience of patients with chronic respiratory failure.

Educational aims

To provide an overview of the rationale for delivering care via telemedicine for patients with chronic respiratory failure.To provide the evidence base for establishing a telemedicine service.To highlight the potential opportunities and challenges in delivering a telemedicine service for patients with chronic respiratory failure.

Smart Textiles and Sensorized Garments for Physiological Monitoring: A Review of Available Solutions and Techniques

Several wearable devices for physiological and activity monitoring are found on the market, but most of them only allow spot measurements. However, the continuous detection of physiological parameters without any constriction in time or space would be useful in several fields such as healthcare, fitness, and work. This can be achieved with the application of textile technologies for sensorized garments, where the sensors are completely embedded in the fabric. The complete integration of sensors in the fabric leads to several manufacturing techniques that allow dealing with both the technological challenges entailed by the physiological parameters under investigation, and the basic requirements of a garment such as perspiration, washability, and comfort. This review is intended to provide a detailed description of the textile technologies in terms of materials and manufacturing processes employed in the production of sensorized fabrics. The focus is pointed at the technical challenges and the advanced solutions introduced with respect to conventional sensors for recording different physiological parameters, and some interesting textile implementations for the acquisition of biopotentials, respiratory parameters, temperature and sweat are proposed. In the last section, an overview of the main garments on the market is depicted, also exploring some relevant projects under development.