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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.

A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

This paper aims to investigate the role of aripiprazole once monthly as a maintenance treatment in a sample of patients with schizophrenia comorbid with alcohol and substance use disorder (AUD/SUD).

A sample of 24 Italian adult patients has been recruited and treated with aripiprazole once monthly after clinical stabilization with oral aripiprazole during May 2021 and June 2022. Clinical evaluations have been performed at the baseline (T0) and after 12 (T1) and 24 (T2) weeks.

During the study period, an improvement of both the clinical condition and general health from baseline was observed, as well as a reduction of craving for alcohol/substances. However, from T0, the number of patients who continued with this study decreased at T1 (n = 8) and then at T2 (n = 4). No serious adverse events were reported, including changes in weight, lipid/glucose metabolism, electrocardiogram and extra-pyramidal side effects.

Although limited by the high number of drop outs, this observational real-world study provided insights into the use of aripiprazole once monthly among a sample of patients with schizophrenia and comorbid SUD/AUD. Further studies of longer duration and on a larger sample are needed.

The purpose of this manuscript is to detect heart fault using Electrocardiogram. Mutually low and high frequency noises such as electromyography (EMG) and power line interference (PLI) degrades the performance of ECG signals.

The ECG record depicts the procedural electrical movement of the heart, which is non-invasive foot age obtained by placing surface electrodes on designated locations of the patient’s skin. The main concept of this manuscript is to present a novel filtering method to cancel the unwanted noises in ECG signal. Here, intrinsic time scale decomposition (ITD) is introduced to suppress the effect of PLI from ECG signals.

In the existing ITD, the gain control parameter is a constant value; however, in this paper it is an adaptive feature that varies according to certain constraints. Simulation outcomes show that the proposed method effectively reduces the effect of PLI and quantitatively express the effectiveness with different evaluation metrics.

The results found by the proposed method are compared with Fourier decomposition technique and eigen value decomposition methods (EDM) to validate the effectiveness of the proposed method.

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.

This study is dedicated to critically analysing research addressing human resource management (HRM) and the adoption of artificial intelligence (AI) with the purpose of driving development in the field of human resources (HR) at the strategic and managerial level.

A systematic literature review (SLR) was conducted using the Scopus database, which gathered 61 articles between 2002 and 2022. The SLR process has the potential to, in addition to generating knowledge and theories, support and guide policy development and practice in many disciplines.

The results of this study allowed the author to identify three main conclusions: (a) there are four thematic clusters – (i) Strategic HR and AI, (ii) Recruitment and AI, (iii) Training and AI and (iv) Future of work; (b) there is a growing academic interest in studying the implementation of AI to develop the HR sector and (c) the application of AI stands out in the strategic HR and AI cluster as a means of achieving profit maximisation and the overall development of the organisation.

This study is the first SLR to present a strategic and managerial view on AI applications associated with specific HRM dimensions. The study is also the first SLR to identify key trends in the literature, drivers and obstacles to the development of AI in HRM and then place them within the landscape of positive and negative approaches in a framework. Also, as a contribution, the study has practical implications for HR managers and practitioners in adopting AI as a decision support in the area's processes.

An increasing aging population and an increasing number of people suffering from musculoskeletal disorders have increased the demand for wearable robots. Comfortable, wearable robots that can be worn like clothing are currently being investigated. However, the embedded components may be displaced owing to the flexibility of the fabrics, which can lower the sensing accuracy and limit natural body movements. This study aims to develop clothing-type wearable platforms to minimize the displacement of embedded components such as sensors and actuators while maintaining comfort.

Four designs were developed using materials with different seam lines, that can serve as anchoring details, and flatlock stitches considering body movements and musculoskeletal structures. The wear evaluation experiment was filmed using a speed camera and analyzed using the TimeViewer software and SPSS 26.0. Based on these results, four clothing-type wearable platform designs were developed.

The variation in the location of a point in the armhole among the designs was marginal. Participants were satisfied with the functionality, practicality, wearability, efficiency and ease of use of the developed designs. A final clothing-type wearable platform was developed by applying a design with the least change in location, a suitable design for each area and wear comfort.

The results of this study contribute to the development of wearable robots by establishing clothing design data to minimize changes in sensor and actuator movements.

Drawing on the three flow-channel model and the stimulus–organism–response model, this research aims to understand how the influence of interactivity on purchase intention is mediated by the state of flow.

This research entails a neurophysiological experiment combined with a survey-based study. This study analyzes, first, participants' heart rate variability through the parasympathetic and sympathetic indexes during five different activities, and, second, the mediating effect of flow through a structural equation model.

This study contributes to research on the role of flow in interactive marketing. The findings reveal that flow state is associated with less stressful activities, such as navigating highly interactive websites and playing online games. This research confirms the mediating effect of flow between interactivity and purchase intention.

This paper contributes to interactive marketing and flow theory by combining traditional methods with a peripheral physiological technique that draws on neuroscience. In addition, it confirms the positive but indirect effect of interactivity on purchase intention in online contexts.

This study aims to explore how knowledge management (KM) influences the intellectual capital (IC) of organizations operating in health care and how IC and knowledge-sharing (KS) can contribute to the achievement of sustainable development in health systems. Notably, this study focuses on telemedicine, investigating how relational capital contributes to KS in the context of remote care services.

To comply with the paper’s aim, the authors use a qualitative research method based on a polar case study suitable for IC in health-care studies. More precisely, this study analyzes a nonprofit organization that, for over 15 years, has offered a free multispecialist teleconsultation service to answer medical questions from the most disadvantaged places in the world.

The findings show that the KM significantly contributes to the IC of organizations. Indeed, it improves the data management and transmission system, it increases performance flexibility in times of resource scarcity without compromising business objectives and it can attract new human resources even when not motivated by selfish goals (volunteer physicians).

This research contributes to studies on IC in health care by focusing on the contribution of telemedicine to the creation of IC. In particular, this work emphasizes the ability of telemedicine to develop and share knowledge in disadvantaged areas of the world. Moreover, in the current context, still strongly permeated by the health emergency generated by the pandemic and recently by the war in Eastern Europe, the importance of such assistance and diagnosis grows.

The conclusions the research findings lead may guide policymakers toward a policy supporting telemedicine. It would alleviate general health-care costs and completely revolutionize light health care’s role. Moreover, reducing socioeconomic distances, improving access to care and applying innovative technologies for sharing outcomes foster balanced socioeconomic development and knowledge dissemination.

This research has shown how telemedicine represents a new successful business model even in times of crisis. The organizational model makes it possible to offer cutting-edge specialized care, contain costs, easily reach disadvantaged areas of the planet, strengthen the skills and autonomy of the most backward countries through a process of KS and push the structures operating there to interact with those in advanced countries.