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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.

Selecting and using the same health monitoring devices for a particular problem is a tedious task. This paper aims to provide a comprehensive review of 40 research papers giving the Smart health monitoring system using Internet of things (IoT) and Deep learning.

Health Monitoring Systems play a significant role in the healthcare sector. The development and testing of health monitoring devices using IoT and deep learning dominate the healthcare sector.

In addition, the detailed conversation and investigation are finished by techniques and development framework. Authors have identified the research gap and presented future research directions in IoT, edge computing and deep learning.

The gathered research articles are examined, and the gaps and issues that the current research papers confront are discussed. In addition, based on various research gaps, this assessment proposes the primary future scope for deep learning and IoT health monitoring model.

A new research domain known as the Quantified Self has recently emerged and is described as gaining self-knowledge through using wearable technology to acquire information on self-monitoring activities and physical health related problems. However, very little is known about the impact of time window models on discovering self-quantified patterns that can yield new self-knowledge insights. This paper aims to discover the self-quantified patterns using multi-time window models.

This paper proposes a multi-time window analytical workflow developed to support the streaming k-means clustering algorithm, based on an online/offline approach that combines both sliding and damped time window models. An intervention experiment with 15 participants is used to gather Fitbit data logs and implement the proposed analytical workflow.

The clustering results reveal the impact of a time window model has on exploring the evolution of micro-clusters and the labelling of macro-clusters to accurately explain regular and irregular individual physical behaviour.

The preliminary results demonstrate the impact they have on finding meaningful patterns.

Construction organizations must maintain a productive workforce without sacrificing their health and safety. The global construction sector loses billions of dollars yearly to poor health and safety practices. This study aims to investigate benefits derivable from using wearable technologies to improve construction health and safety. The study also reports the challenges associated with adopting wearable technologies.

The study adopted a quantitative design, administering close-ended questions to professionals in the Nigerian construction industry. The research data were analysed using descriptive and inferential statistics.

The study found that the critical areas construction organizations can benefit from using WSDs include slips and trips, sensing environmental concerns, collision avoidance, falling from a high level and electrocution. However, key barriers preventing the organizations from adopting wearable technologies are related to cost, technology and human factors.

The time and cost lost to H&S incidents in the Nigerian construction sector can be reduced by implementing the report of this study.

Studies on WSDs have continued to increase in developed countries, but Nigeria is yet to experience a leap in the research area. This study provides insights into the Nigerian reality to provide directions for practice and theory.

This paper aims to investigate the influence of health beliefs and trust by senior adults as associated with the perceived ease of use and perceived usefulness, for the acceptance of smart technology with a focus on smartwatch technology.

Structural equation modeling is used to conceptualize the model using survey data collected from 243 randomly selected senior adults 60+ years of age.

This paper presents that perceived usefulness, perceived ease of use, trust and health belief are direct and indirect predictors of senior adults’ technology acceptance and intention to use smartwatch technology.

The study reveals the moderator effect of social influence on relation between perceived usefulness, perceived ease of use and intention to use. The authors highlight the effect of health belief and trust on perceived usefulness and perceived ease of use and the role of intention to use smartwatch technology.

The authors contribute bridging developers of health technologists and senior adults as end-user perspectives. For marketing of health-care technology products, specifically smartwatch, to seniors, a focus on health beliefs and trust is essential to build, maintain and improve perceived usefulness and perceived ease of use.

The present study contributes empirical evidence to the literature on factors affecting the acceptance of the smartwatch technology by senior adults.

The metaverse, through artificial intelligence (AI) systems and capabilities, allows considerable data analysis in the workplace, largely exceeding traditional people analytics data collection. While concerns over surveillance and issues associated with privacy and discrimination have been raised, the metaverse has the potential to offer opportunities associated with fairer assessment of employee performance and enhancement of the employee experience, especially with respect to gender and race, inclusiveness and workplace equity. This paper aims at shedding light on the diversity, equity and inclusion (DEI) opportunities and challenges of implementing the metaverse in the workplace, and the role played by AI.

This paper draws on our past research on AI and the metaverse and provides insights addressed to human resources (HR) scholars and practitioners.

Our analysis of AI applications to the metaverse in the workplace sheds light on the ambivalent role of and potential trade-offs that may arise with this emerging technology. If used responsibly, the metaverse can enable positive changes concerning the future of work, which can promote DEI. Yet, the same technology can lead to negative DEI outcomes if implementations occur quickly, unsupervised and with a sole focus on efficiencies and productivity (i.e. collecting metrics, models etc.).

Managers and HR leaders should try to be first movers rather than followers when deciding if (or, better, when) to implement metaverse capabilities in their organizations. But how the metaverse is implemented will be strategic. This involves choices concerning the degree of invasive/pervasive monitoring (internal) as well as make or buy decisions concerning outsourcing AI capabilities.

Our paper is one among few (to date) that discusses AI capabilities in the metaverse at the intersection of the HR and information systems(IS) literature and that specifically tackles DEI issues. Also, we take a “balanced” approach when evaluating the metaverse from a DEI perspective. While most studies either demonize or celebrate these technologies from an ethical and DEI standpoint, we aim to highlight challenges and opportunities, with the goal to guide scholars and practitioners towards a responsible use of the metaverse in organizations.

The purpose of this paper is to understand the applications of metaverse-related technologies in US urban libraries, explore excellent cases of US urban libraries' practices in using metaverse-related technologies to serve patrons and try to find the factor that may affect the application of metaverse-related technologies in libraries at this stage.

To gather information about how and whether libraries use metaverse-related technologies such as three-dimensional (3D) technology, radio frequency identification (RFID), virtual reality augmented reality (AR) and artificial intelligence (AI) in their services. Firstly, the authors visit 150 US urban library websites that are members of the Urban Libraries Council. Secondly, the authors calculate the proportion of services provided by urban libraries that use metaverse-related technologies and introduce outstanding cases. Lastly, the authors discuss the factors that influence the application of metaverse-related technologies in urban libraries in the United States based on data published by the Institute of Museum and Library Services.

Metaverse-related technologies have been widely used in US urban libraries, but there are differences in the popularity of the applications of different technologies. In all, 84% of libraries use 3D technology, mainly in 3D printing services and 3D model building services; 76% of libraries use virtual and augmented reality technologies in their services, mainly concentrated in head-mounted VR device experiences, AR device experiences, virtual tours and virtual exhibitions; 62% of libraries use Internet of things (IoT) technology, mainly in self-checkout machines and book location services. However, AI technologies are less used in libraries, with 28% of libraries mentioning the applications of AI in their services, mainly focusing on intelligent search, virtual assistants and robot librarians. In addition, this study finds that library operating expenditures and population served do not affect the application of metaverse-related technologies in libraries.

This paper provides updated statistical data on the use of metaverse-related technologies in US urban libraries and aims to help library managers understand the overall applications and best practices. With this as an inspiration, they could formulate corresponding development plans to better serve their communities with metaverse-related technologies.

The purpose of this paper is to identify the potential and the challenges for different firms in adopting an AI-first strategy. The study attempts to discern if any company can prioritize AI at the forefront of their strategic plans.

Drawing from illustrative examples from well-known AI-leaders like Netflix and Spotify, as well as from upcoming AI startups and industry incumbents, the paper explores the strategic role of AI in core business processes and customer value creation. It also discusses the advent and implications of generative AI tools since late 2022 to firms’ business strategies.

The authors identify three types of AI-first strategies, depending on firms’ starting points: digital tycoon, niche carver and asset augmenter. The authors discuss how each strategy can aim to achieve data, algorithmic and execution advantages, and what the strategic bottlenecks and risks are within each strategy.

To the best of the authors’ knowledge, this paper is the first to systematically describe how companies can form “AI-first” strategies from different starting points. This study includes actionable examples from known industry players to more emerging startups and industrial incumbents.

This study aims to produce textile-like surfaces using fused deposition modelling (FDM) 3D printers and create a garment collection.

Experiments were conducted using different types of materials in FDM 3D printers until the sufficient flexibility was achieved to create textile-like structures. During the research, properties of polylactic acid (PLA), acrylonitrile butadiene styrene (ABS) and thermoplastic polyurethane (TPU) were observed. Geometrical patterns were printed and each of them gave a different result depending on the pattern. Based on the information obtained from the experiments, a garment collection with four total looks was designed inspired by Vivaldi’s “Four Seasons”.

Among the materials used, TPU, a flexible filament, yielded the best results. Because of the rigid properties of PLA and ABS, chain-like structures were printed to create relatively flexible surfaces, but the results were still not successful enough to create a clothing material. Therefore, TPU was preferred for the garment material selection.

In this study, combinations of 3D printed flexible structures and different types of fabrics were used to create a garment collection. It was concluded that, with the right material selection, 3D printing can be used as an alternative method to create a new aesthetic language in fashion design.

The purpose of this paper is to secure health data collection and transmission (SHDCT). In this system, a native network consists of portable smart devices that interact with multiple gateways. It entails IoMT devices and wearables connecting to exchange sensitive data with a sensor node which performs the aggeration process and then communicates the data using a Fog server. If the aggregator sensor loses the connection from the Fog server, it will be unable to submit data directly to the Fog server. The node transmits encrypted information with a neighboring sensor and sends it to the Fog server integrated with federated learning, which encrypts data to the existing data. The fog server performs the operations on the measured data, and the values are stored in the local storage area and later it is updated to the cloud server.

SHDCT uses an Internet-of-things (IoT)-based monitoring network, making it possible for smart devices to connect and interact with each other. The main purpose of the monitoring network has been in the collection of biological data and additional information from mobile devices to the patients. The monitoring network is composed of three different types of smart devices that is at the heart of the IoT.

It has been addressed in this work how to design an architecture for safe data aggregation in heterogeneous IoT-federated learning-enabled wireless sensor networks (WSNs), which makes use of basic encoding and data aggregation methods to achieve this. The authors suggest that the small gateway node (SGN) captures all of the sensed data from the SD and uses a simple, lightweight encoding scheme and cryptographic techniques to convey the data to the gateway node (GWN). The GWN gets all of the medical data from SGN and ensures that the data is accurate and up to date. If the data obtained is trustworthy, then the medical data should be aggregated and sent to the Fog server for further processing. The Java programming language simulates and analyzes the proposed SHDCT model for deployment and message initiation. When comparing the SHDCT scheme to the SPPDA and electrohydrodynamic atomisation (EHDA) schemes, the results show that the SHDCT method performs significantly better. When compared with the SPPDA and EHDA schemes, the suggested SHDCT plan necessitates a lower communication cost. In comparison to EHDA and SPPDA, SHDCT achieves 4.72% and 13.59% less, respectively. When compared to other transmission techniques, SHDCT has a higher transmission ratio. When compared with EHDA and SPPDA, SHDCT achieves 8.47% and 24.41% higher transmission ratios, respectively. When compared with other ways it uses less electricity. When compared with EHDA and SPPDA, SHDCT achieves 5.85% and 18.86% greater residual energy, respectively.

In the health care sector, a series of interconnected medical devices collect data using IoT networks in the health care domain. Preventive, predictive, personalized and participatory care is becoming increasingly popular in the health care sector. Safe data collection and transfer to a centralized server is a challenging scenario. This study presents a mechanism for SHDCT. The mechanism consists of Smart healthcare IoT devices working on federated learning that link up with one another to exchange health data. Health data is sensitive and needs to be exchanged securely and efficiently. In the mechanism, the sensing devices send data to a SGN. This SGN uses a lightweight encoding scheme and performs cryptography techniques to communicate the data with the GWN. The GWN gets all the health data from the SGN and makes it possible to confirm that the data is validated. If the received data is reliable, then aggregate the medical data and transmit it to the Fog server for further process. The performance parameters are compared with the other systems in terms of communication costs, transmission ratio and energy use.