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The purpose of this paper is to develop a user friendly, wearable pain management system by optimizing CAD embroidery parameters for manufacturing high performance dry transcutaneous electrical neural stimulation (TENS) electrodes.

User-centered design methodology is employed to identify user needs related to TENS devices. Optimization of CAD embroidery parameters was done by measuring and calculating resistance and signal-to-noise values for electrodes manufactured with different conductive thread, stitch pattern, and stitch density types.

Characteristics of the conductive thread such as thickness and irregularity, embroidery stitch pattern, stitch density therefore the amount of conductive thread used all effect resistance values and signal-to-noise values of TENS electrodes. Low resistance of TENS electrode surface does not mean high signal-to-noise ratio and high TENS signal quality. Satin stitch type with low stitch density provides the best resistance and signal-to-noise ratio for a TENS electrode.

This study reported the design process of a wearable pain management system with a focus on optimization of embroidery manufacturing parameters for development of TENS electrodes. The design process not only required technical optimization but also understanding user problems related to use of conventional TENS devices. Proposed end product is a user friendly, electronic textile based, wireless wearable pain management system in different forms suitable for major pain areas such as knee, elbow and neck.

This paper aims to present investigations of the influence of sewing and adhesive bonding technology on the aesthetic, mechanical and conductive properties of the e-textile package. Commercially available conductive textiles are tested for the production of e-textile package by most common cut-and-sewn clothing production technologies.

Sewing, adhesive bonding and seam sealing technologies used to obtain e-textile packages with woven and knitted conductive textiles. Produced e-textile packages described in terms of thickness, bending rigidity and general appearance. Exploitation properties of prepared samples tested by cycle tensile experiment and discussed on the basis of variation of linear electrical resistance property.

Research has shown that a reliable e-textile package can be obtained by applying cut-and-sew technology for conductive tracks of silver coated woven and knitted material. Seam sealing by thermoplastic polymer layer has an impact on the electrical and deformation properties of the samples. To create attractive smart clothing design, the appropriate joining method and its technological parameters must be chosen to ensure the durability and safety of e-textile packages.

The findings of the research are of substantial value for the production of e-textiles by cut-and-sewn technologies. The required shape of the conductive textile element for various applications can be cut and joined to the garment parts using traditional sewing or adhesive bonding techniques.

Embroidery as a textile embellishment technique plays an important role in people's daily life. Esthetic embroidery artworks possess cultural values. With the development of robotics and artificial intelligence (AI), these technologies have been studied and applied in the embroidery process. This study aims to survey how these technologies facilitate embroidery from different aspects.

This paper surveys how the technologies of robotics and AI are applied in the embroidery field. The applications are mainly reviewed from three aspects: computerized robotic embroidery systems has been widely used for the mass production of embroidered textiles, the advanced technological systems and techniques have greatly facilitated the development of smart textiles and the artificial intelligence plays an important role in the inheritance, innovation and protection of traditional handicraft artwork of embroidery.

The programmable robotic embroidery machines have greatly improved the production efficiency of embroidered textiles and promoted the development of electronic textiles. The AI, mainly the deep learning technology, brings significant benefits to esthetic embroidery creation. Technology-based embroidery has become a hot research topic in the field of textiles.

This paper summarizes the application of robotics and AI technologies in the field of embroidery, which provides readers a comprehensive and systematic understanding about the research progress of modern technology-oriented embroidery. This helps readers gain inspiration from the technology perspectives.

Carpal tunnel syndrome (CTS) is a hand disease caused by the pressing of the median nerve present in the palmar side of the wrist. It causes severe pain in the wrist, triggering disturbance during sleep. Different products like splints, braces and gloves are available in the market to alleviate this disease but there was still a need to improve the wearability, comfort and cost of the product. This study was about designing a comfortable and cost-effective wearable system for mild-to-moderate CTS. Transcutaneous electrical nerve stimulation (TENS) therapy has been used to reduce the pain in the wrist.

After simulation by using Proteus software (which allowed the researchers to draw and simulate electrical circuits using ISIS, ARES and PCB design tools virtually), the circuit with optimum frequency, i.e. 33 Hz was selected, and the circuit was developed on a printed circuit board (PCB). The developed circuit was integrated successfully into the half glove structure.

The developed product had good thermophysiological comfort and hand properties as compared to the commercially available product of the same kind. In vivo testing (It involves the testing with living subjects like animals, plants or human beings) was performed which resulted in 85% confirmed viability of the product against CTS. A glove with an integrated circuit was developed successfully to accommodate various sizes without any sex specifications in a cost-effective way to mitigate the issue of CTS.

Industrial workers, individuals frequently using their hands or those diagnosed with CTS may wish to use this product as therapy. The attention could not be paid to the aesthetic or visual appeal of the developed product.

A very comfortable glove with integrated TENS electrodes was developed successfully to accommodate various sizes without any sex specifications in a cost-effective way to mitigate the issues of CTS.

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

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.

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.

The purpose of this paper is to expand current knowledge about the recent trend of wearable technology to assess both its potential in the work environment and the challenges concerning the utilisation of wearables in the workplace.

After establishing exclusion and inclusion criteria, an independent systematic search of the ACM Digital Library, IEEE Xplore, ScienceDirect and Web of Science databases for relevant studies was performed. Out of a total of 359 articles, 34 met the selection criteria.

This review identifies 23 categories of wearable devices. Further categorisation of the devices based on their utilisation shows they can be used in the work environment for activities including monitoring, augmenting, assisting, delivering and tracking. The review reveals that wearable technology has the potential to increase work efficiency among employees, improve workers’ physical well-being and reduce work-related injuries. However, the review also reveals that technological, social, policy and economic challenges related to the use of wearable devices remain.

Many studies have investigated the benefits of wearable devices for personal use, but information about the use of wearables in the work environment is limited. Further research is required in the fields of technology, social challenges, organisation strategies, policies and economics to enhance the adoption rate of wearable devices in work environments.

Previous studies indicate that occupational stress and injuries are detrimental to employees’ health; this paper analyses the use of wearable devices as an intervention method to monitor or prevent these problems. Introducing a categorisation framework during implementation may help identify which types of device categories are suitable and could be beneficial for specific utilisation purposes, facilitating the adoption of wearable devices in the workplace.

This study aims to conduct text mining and semantic network analysis of muscle-supportive and posture-corrective wearable robots for the elderly to understand key terms related to the topic and to identify considerations for developing these types of clothing.

The authors searched and identified the key terms wearable robot, muscle-supportive, posture correction and elderly using the text-mining software Textom to extract terms as well as the network analysis software UCINET 6 to process and visualize the relationships among the terms. The authors compared and analyzed the term frequency (TF), the TF-inverse document frequency and the degree centrality of the terms, and the authors visualized and summarized the terms using NetDraw.

The key terms and their relationships in 3–4 groups were identified: wearable robot, muscle-supportive, posture correction and elderly. The authors identified the aspects of designing muscle-supportive and posture-corrective wearable robots for the elderly.

This study contributes to the field of muscle-supportive clothing and wearable robotics by deriving insights into what people are discussing and interested in, and by offering recommendations when developing these types of clothing for the elderly.