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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.

Solar cells could make textile-based wearable systems energy independent without the need for battery replacement or recharging; however, their laundry resistance, which is prerequisite for the product acceptance of e-textiles, has been rarely examined. This paper aims to report a systematic study of the laundry durability of solar cells embedded in textiles.

This research included small commercial monocrystalline silicon solar cells which were encapsulated with functional synthetic textile materials using an industrially relevant textile lamination process and found them to reliably endure laundry washing (ISO 6330:2012). The energy harvesting capability of eight textile laminated solar cells was measured after 10–50 cycles of laundry at 40 °C and compared with light transmittance spectroscopy and visual inspection.

Five of the eight textile solar cell samples fully maintained their efficiency over the 50 laundry cycles, whereas the other three showed a 20%–27% decrease. The cells did not cause any visual damage to the fabric. The result indicates that the textile encapsulated solar cell module provides sufficient protection for the solar cells against water, washing agents and mechanical stress to endure repetitive domestic laundry.

This study used rigid monocrystalline silicon solar cells. Flexible amorphous silicon cells were excluded because of low durability in preliminary tests. Other types of solar cells were not tested.

A review of literature reveals the tendency of researchers to avoid standardized textile washing resistance testing. This study removes the most critical obstacle of textile integrated solar energy harvesting, the washing resistance.

To simplify the circuit design and control complexity of the magnetic coupling resonant wireless charging system, the radio energy transmission constant current and constant voltage charging is realized.

The purpose of this study is to simplify the circuit design and control complexity of the magnetic coupling resonance wireless charging system, in order to achieve constant current and constant voltage charging for wireless energy transmission. First, the principle of LCC/S-S compensation structure is analyzed, and the equivalent mathematical model is established; then, the system characteristics under constant current and constant voltage mode are analyzed, and the design method of system parameters is given; finally, a simulation and experimental system is built to verify the correctness and feasibility of the theoretical analysis.

The results show that the proposed hybrid topology can achieve a constant current output of 2 A and a constant voltage output of 30 V under variable load conditions, and effectively suppress the current distortion problem under light load conditions. The waveform distortion rate of the inverter current is reduced from 33.97% to 10.45%.

By changing the high-order impedance characteristics of the compensation structure, the distortion of the current waveform under light load is suppressed, and the overall stability and efficiency of the system are improved.

This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.

Some studies have been reported in the past on diaphragmatic hernia (DH) surgery techniques using additive manufacturing (AM) technologies, symptoms of a hernia and post-surgery complications. But hitherto little has been reported on bibliographic analysis (BA) for health monitoring of bovine post-DH surgery for long-term management. Based on BA, this study aims to explore the sensor fabrication integrated with innovative AM technologies for health monitoring assistance of bovines post-DH surgery.

A BA based on the data extracted through the Web of Science database was performed using bibliometric tools (R-Studio and Biblioshiny).

After going through the BA and a case study, this review provides information on various 3D-printed meshes used over the sutured site and available Internet of Things-based solutions to prevent the recurrence of DH.

Research gaps exist for 3D-printed conformal sensors for health monitoring of bovine post-DH surgery.

Drawing on work from home (WFH), job demand-control and street-level bureaucracy literature streams, this paper specifically focuses on the emerging trend of WFH for public sector employees in a developing country context of Egypt.

The empirical sample comprises focus group discussions with a total of 40 public sector employees in Egypt. Thematic analysis was subsequently used on focus group discussion transcripts to bring out main themes linked to this topic.

Our findings show that employee (marginal discretion power, pharaonism, corruption), citizen (unfamiliarity with digital services) and country (lack of proper info-structure, overstaffing in the public sector)- level challenges hinder and/or slow down the potential for WFH in Egyptian public sector.

A major implication of our paper relates to highlighting the criticality of e-governance and WFH for public sector employees, as well as highlighting multilevel challenges associated with those. At the same time, socio-economic and political consequences of offering such options need to be considered in a country like Egypt where most public organisations are overstaffed, and those employees lack modern day employability skills. Hence, there needs to be an open debate in countries such as Egypt on the consequences of e-governance and WFH and whether it may facilitate delivering citizen services digitally. Also, high power distance culture plays a role in this context, and any change cannot be successful unless that specific aspect is confronted.

This study contributes to the emerging WFH literature by being one of the pioneering studies to offer a multilevel (micro, meso and macro) assessment of this phenomenon in the under-researched fragile developing country’s context.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

Customers today expect businesses to cater to their individual needs by tailoring the products they purchase to their own preferences. The term “Industry 5.0” refers to a new wave of manufacturing that aims to meet each customer's unique demands. Even while Industry 4.0 allowed for mass customization, that wasn't good enough before, customers today demand individualized products at scale, and Industry 5.0 is driving the transition from mass customization to mass personalization to meet these demands. It caters to the individual needs of each consumer by meeting their demands. More specialized components for use in medicine are made possible by the widespread customization made possible by Industry 5.0. These individualized parts are included into the medical care of the patient to meet their specific needs and preferences. In the current medical revolution, an enabling technology of Industry 5.0 can produce medical implants, artificial organs, bodily fluids, and transplants with pinpoint accuracy. With the advent of AI-enabled sensors, we now live in a world where data can be swiftly analyzed. Machines may be programmed to make complex choices on the fly. In the medical field, these innovations allow for exact measurement and monitoring of human body variables according to the individual's needs. They aid in monitoring the body's response to training for peak performance. It allows for the digital dissemination of accurate healthcare data networks. In order to collect and exchange relevant patient data, every equipment is online.

The aim of this paper is to determine whether the dominant integrated water resources management (IWRM) paradigm within which the Pra River Basin is managed holds the key to address the current water security and sustainability issues in Southwestern Ghana.

This study employed a literature review developed based on water security and sustainability studies as well as normative scenarios from the broad scenario planning methodology. The study builds on Wæver’s Theory of Securitization and the Utilitarian theory to protect water bodies through the use of artificial intelligence (AI).

Insights on introducing innovative environmental sustainability technology are presented and propose the Pra-integrated smart water security management decision-making system that uses visual inspections, noise sensors, the potential of hydrogen (pH) probe sensor, real-time collection of hydrological data (streamflow) and wireless transmission of the data in real-time at the basin level. This serves as a robust tool for managing the basin’s sustainable development ecosystem by using AI to protect water bodies against illegal mining.

The proposed innovative environmental technology which is the first of its kind is meant to gain a better understanding of pollution incidents and respond quickly to them by integrating AI and Internet of Things (IoT) technologies with traditional IWRM practices. This addresses water security in the Pra Basin, supports policy development and innovation, strengthens the goal of the government to protect water resources against pollution and contributes to the African Water Vision and the United Nations’ Agenda 2030 Sustainable Development Goals 3 and 6.