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This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

Soft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart.

In continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system.

The tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics.

Tracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.

Pervasive analytics act as a prominent role in computer-aided prediction of non-communicating diseases. In the early stage, arrhythmia diagnosis detection helps prevent the cause of death suddenly owing to heart failure or heart stroke. The arrhythmia scope can be identified by electrocardiogram (ECG) report.

The ECG report has been used extensively by several clinical experts. However, diagnosis accuracy has been dependent on clinical experience. For the prediction methods of computer-aided heart disease, both accuracy and sensitivity metrics play a remarkable part. Hence, the existing research contributions have optimized the machine-learning approaches to have a great significance in computer-aided methods, which perform predictive analysis of arrhythmia detection.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

The purpose of this paper is to design and manufacture an intelligent 3D printed sensor to monitor the re-occurrence of diaphragmatic hernia (DH; after surgery) in bovines as an Internet of Things (IOT)-based solution.

The approach used in this study is based on a bibliographic analysis for the re-occurrence of DH in the bovine after surgery. Using SolidWorks and ANSYS, the computer-aided design model of the implant was 3D printed based on literature and discussions on surgical techniques with a veterinarian. To ensure the error-proof design, load test and strain–stress rate analyses with boundary distortion have been carried out for the implant sub-assembly.

An innovative IOT-based additive manufacturing solution has been presented for the construction of a mesh-type sensor (for the health monitoring of bovine after surgery).

An innovative mesh-type sensor has been fabricated by integration of metal and polymer 3D printing (comprising 17–4 precipitate hardened stainless steel and polyvinylidene fluoride-hydroxyapatite-chitosan) without sacrificing strength and specific absorption ratio value.

The purpose of this paper is to develop a prototype of a wearable medical device in the form of a bandage with a real-time data monitoring platform, which can be used domestically for diabetic patients to identify the possibility of foot ulceration at the early stage.

The prototype can measure blood volumetric change and temperature variation in the forefoot area simultaneously. The waveform extracted using a pulsatile-blood-flow signal was used to assess blood perfusion-related information, and hence, predict ischemic ulcers. The temperature difference between ulcerated and the reference was used to predict neuropathic ulcers. The medical device can be used as a bandage during the application wherein the sensory module is placed inside the hollow pocket of the bandage. A platform was developed through a mobile application where doctors can extract real-time information, and hence, determine the possibility of ulceration.

The height of the peaks in the pulsatile-blood-flow signal measured from the subject with foot ischemic ulcers is significantly less than that of the subject without ischemic ulcers. In the presence of ischemic ulcers, the captured waveform flattens. Therefore, the blood perfusion from arteries to the tissue of the forefoot is considerably low for the subject with ischemic ulcers. According to the temperature difference data measured over 25 consecutive days, the temperature difference of the subject with neuropathic ulcers occasionally exceeded the 4 °F range but mostly had higher values closer to the 4 °F range. However, the temperature difference of the subject who had no complications of neuropathic ulcers did not exceed the 4 °F range, and the majority of the measurements occupy a narrow range from −2°F to 2 °F.

The proposed prototype of wearable medical apparatus can monitor both temperature variation and pulsatile-blood-flow signal on the forefoot simultaneously and thereby predict both ischemic and neuropathic diabetes using a single device. Most importantly, the wearable medical device can be used domestically without clinical assistance with a real-time data monitoring platform to predict the possibility of ulceration and the course of action thereof.

This paper aims to examine the global pattern of growth and development of eHealth research based on publication headcount, and analysis of the characteristics, of the keywords used by authors and indexers to represent their research content during 1945–2019.

This study adopted a bibliometric research design and a quantitative approach. The source of the data was Elsevier’s Scopus database. The search query involved multiple search terms because researchers’ choice of keywords varies very significantly. The search for eHealth research publications was limited to conference papers and research articles published before 2020.

eHealth originated in the late 1990s, but it has become an envelope term for describing much older terms such as telemedicine, and its variants that originated much earlier. The keywords were spread through the 27 Scopus Subject Areas, with medicine (44.04%), engineering (12.84%) and computer science (11.47%) leading, while by Scopus All Science Journal Classification Health Sciences accounted for 55.83% of the keywords. Physical sciences followed with 30.62%. The classifications social sciences and life sciences made only single-digit contributions. eHealth is about meeting health needs, but the work of engineers and computer scientists is very outstanding in achieving this goal.

This study demonstrates that eHealth is an unexplored aspect of health literature and highlights the nature of the accumulated literature in the area. It further demonstrates that eHealth is a multidisciplinary area that is attractive to researchers from all disciplines because of its sensitive focus on health, and therefore requires pooling and integration of human resources and expertise, methods and approaches.

A sense of collective free-thinking with tangible goals makes co-creation an enlightening experience. Yet despite the freedom and organic flow of the methodology, there remain barriers to deploying co-creation in the real-world context. The aim was to understand the barriers and solutions to co-creation, reflect on applying co-creation in practice and co-create an applicable framework for co-creation.

These reflections and conceptual developments were completed using a Participatory Action Research Approach through the co-creation of the Erasmus+ funded Co-creating Welfare course.

Results presented are centric to the experiences in the United Kingdom but led to application at an international level. Problem formulation led to solutions devised about who should co-create, what co-creation aims to achieve, how to receive management buy-in, co-creating beyond the local face to face context and evaluation.

The Three Co’s Framework is proposed using the outline of: Co-Define, Co-Design and Co-Refine. Those who take part in co-creation processes are recommended to be called co-creators, with less focus on “empowerment” and more about facilitating people to harness the power they already have. Utilising online and hybrid delivery methods can be more inclusive, especially in response to the COVID-19 pandemic. The use of co-creation needs to be evaluated more moving forwards, as well as the output co-created.

The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

The purpose of this paper is to report on the dynamics of “identity leadership” with a quality improvement project undertaken by an International Medical Graduate (IMG) from Sri Lanka, on a two year Medical Training Initiative (MTI) placement in the National Health Service (NHS) [Academy of Medical Royal Colleges (AoMRC), 2017]. A combined MTI rotation with an integrated Fellowship in Quality Improvement (Subedi et al., 2019) provided the driver to implement the HEART score (HS) in an NHS Emergency Department (ED) in the UK. The project was undertaken across ED, Acute Medicine and Cardiology at the hospital, with stakeholders emphasizing different and conflicting priorities to improve the pathway for chest pain patients.

A social identity approach to leadership provided a framework to understand the insider/outsider approach to leadership which helped RH to negotiate and navigate the conflicting priorities from each departments’ perspective. A staff survey tool was undertaken to identify reasons for the lack of implementation of a clinical protocol for chest pain patients, specifically with reference to the use of the HS. A consensus was reached to develop and implement the pathway for multi-disciplinary use of the HS and a quality improvement methodology (with the use of plan do study act (PDSA) cycles) was used over a period of nine months.

The results demonstrated significant improvements in the reduction (60%) of waiting time by chronic chest pain patients in the ED. The use of the HS as a stratified risk assessment tool resulted in a more efficient and safe way to manage patients. There are specific leadership challenges faced by an MTI doctor when they arrive in the NHS, as the MTI doctor is considered an outsider to the NHS, with reduced influence. Drawing upon the Social Identity Theory of Leadership, NHS Trusts can introduce inclusion strategies to enable greater alignment in social identity with doctors from overseas.

More than one third of doctors (40%) in the English NHS are IMGs and identify as black and minority ethnic (GMC, 2019a) a trend that sees no sign of abating as the NHS continues its international medical workforce recruitment strategy for its survival (NHS England, 2019; Beech et al., 2019). IMGs can provide significant value to improving the NHS using skills developed from their own health-care system. This paper recommends a need for reciprocal learning from low to medium income countries by UK doctors to encourage the development of an inclusive global medical social identity.

This quality improvement research combined with identity leadership provides new insights into how overseas doctors can successfully lead sustainable improvement across different departments within one hospital in the NHS.