Workplace exposure is an important source of ill health. The use of wearable sensors and sensing technologies may help improve and maintain worker health, safety, and wellbeing. Input from workers should inform the integration of these sensors into workplaces. We developed an online survey to understand the acceptability of wearable sensor technologies for occupational health and safety (OSH) management. The survey was disseminated to members of OSH-related organizations, mainly in the United Kingdom and the Netherlands. There were 158 respondents, with over half (n = 91, 58%) reporting current use of wearable sensors, including physical hazards (n = 57, 36%), air quality (n = 53, 34%), and location tracking (n = 36, 23%), although this prevalence likely also captures traditional monitoring equipment. There were no clear distinctions in wearable sensor use between the reported demographic and occupational characteristics, with the exception that hygienists were more likely than non-hygienists (e.g. safety professionals) to use wearable sensors (66% versus 34%). Overall, there was an interest in how sensors can help OSH professionals understand patterns of exposure and improve exposure management practices. Some wariness was expressed primarily around environmental and physical constraints, the quality of the data, and privacy concerns. This survey identified a need to better identify occupational situations that would benefit from wearable sensors and to evaluate existing devices that could be used for occupational hygiene. Further, this work underscores the importance of clearly defining \"sensor\" according to the occupational setting and context.

Copolymers based on vinylidene fluoride are potential materials for ferroelectric memory elements. The trend in studies showing that a decrease in the degree of crystallinity can lead to an unexpected increase in the electric breakdown field is noted. An analysis of the literature data reveals that in fluorine-containing ferroelectric polymers, when using a bipolar triangular field, the hysteresis loop has an unclosed shape, with each subsequent loop being accompanied by a decrease in the dielectric response. In this work, the effect of the structure of self-polarized films of copolymers of vinylidene fluoride with tetrafluoroethylene and hexafluoropropylene on breakdown processes was studied. The structure of the polymer films was monitored using infrared spectroscopy (IR) and X-ray diffraction. Kelvin probe force microscopy (KPFM) was applied to characterize the local electrical properties of the polymers. For the films of the first copolymer, which crystallize in the polar β-phase, asymmetry in the dielectric response was observed at fields greater than the coercive field. For the films of the copolymers of vinylidene fluoride with hexafluoropropylene, which crystallize predominantly in the nonpolar α-phase, polarization switching processes have also been observed, but at lower electric fields. The noted phenomena will help to identify the influence of the structure of ferroelectric polymers on their electrical properties.

Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements.

As a fundamental issue in robotics academia and industry, indoor autonomous mobile robots (AMRs) have been extensively studied. For AMRs, it is crucial to obtain information about their working environment and themselves, which can be realized through sensors and the extraction of corresponding information from the measurements of these sensors. The application of sensing technologies can enable mobile robots to perform localization, mapping, target or obstacle recognition, and motion tasks, etc. This paper reviews sensing technologies for autonomous mobile robots in indoor scenes. The benefits and potential problems of using a single sensor in application are analyzed and compared, and the basic principles and popular algorithms used in processing these sensor data are introduced. In addition, some mainstream technologies of multi-sensor fusion are introduced. Finally, this paper discusses the future development trends in the sensing technology for autonomous mobile robots in indoor scenes, as well as the challenges in the practical application environments.

Type 2 diabetes mellitus (T2D) poses a significant global health challenge and demands effective self-management strategies, including continuous blood glucose monitoring (CGM) and lifestyle adaptations. While CGM offers real-time glucose level assessment, the quest for minimizing trauma and enhancing convenience has spurred the need to explore non-invasive alternatives for monitoring vital signs in patients with T2D. Objective: This systematic review is the first that explores the current literature and critically evaluates the use and reporting of non-invasive wearable devices for monitoring vital signs in patients with T2D. Methods: Employing the PRISMA and PICOS guidelines, we conducted a comprehensive search to incorporate evidence from relevant studies, focusing on randomized controlled trials (RCTs), systematic reviews, and meta-analyses published since 2017. Of the 437 publications identified, seven were selected based on predetermined criteria. Results: The seven studies included in this review used various sensing technologies, such as heart rate monitors, accelerometers, and other wearable devices. Primary health outcomes included blood pressure measurements, heart rate, body fat percentage, and cardiorespiratory endurance. Non-invasive wearable devices demonstrated potential for aiding T2D management, albeit with variations in efficacy across studies. Conclusions: Based on the low number of studies with higher evidence levels (i.e., RCTs) that we were able to find and the significant differences in design between these studies, we conclude that further evidence is required to validate the application, efficacy, and real-world impact of these wearable devices. Emphasizing transparency in bias reporting and conducting in-depth research is crucial for fully understanding the implications and benefits of wearable devices in T2D management.

In recent years, with the rapid development of the sports industry, the quality of sports training products on the market is uneven. Problems such as inaccurate detection of athletes\' physical indicators, low comfort of sportswear, and reduced satisfaction with sports equipment often occur. To this end, this article proposes to apply conjugated materials with excellent optical, electrical, thermal and other properties to sports training and sports products, by summarizing the properties of conjugated materials and their applications in sports training, explores the potential of conjugated materials in improving athletes\' training effects, monitoring sports status, and improving sports equipment. This article rates the application of conjugated materials in sports training products in terms of comfort, waterproofness, portability, lightness, aesthetics and breathability. The results showed that the average scores of the 20 sports participants on sportswear were 9.0475, 9.0075, 9.01, 9.025, 9.0325 and 9.04 respectively; the average scores on sports shoes were 9.035, 9.055, 9.02, 9.085, 9.0175 and 8.9975 respectively. Research shows that applying conjugated materials to sports training can improve athletes\' performance and contribute to the better development of sports.

BACKGROUND: Prolonged exclusive breastfeeding is a public health priority and a personal desire by mothers; however, rates are low with milk supply challenges as a predominant cause. Early breastfeeding management at home is key. Milk electrolytes, mainly sodium ions, are accepted as biomarkers of secretory activation processes throughout the first weeks after birth and predictors for prolonged breastfeeding success, although they are not incorporated into routine care practice.
OBJECTIVE: The aim of this study was to test the feasibility of a novel handheld smartphone-operated milk conductivity sensing system that was designed to compute a novel parameter, milk maturation percent (MM%), calculated from milk sample conductivity for tracking individual secretory activation progress in a real-world home setting.
METHODS: System performance was initially evaluated in data collected from laboratory-based milk analysis, followed by a retrospective analysis of observational real-world data gathered with the system, on the spot in an at-home setting, implemented by lactation support providers or directly by mothers (N=592). Data collected included milk sample sensing data, baby age, and self-reported breastfeeding status and breastfeeding-related conditions. The data were retroactively classified in a day after birth-dependent manner. Results were compared between groups classified according to breastfeeding exclusivity and breastfeeding problems associated with ineffective breastfeeding and low milk supply.
RESULTS: Laboratory analysis in a set of breast milk samples demonstrated a strong correlation between the system\'s results and sodium ion levels. In the real-world data set, a total of 1511 milk sensing records were obtained on the spot with over 592 real-world mothers. Data gathered with the system revealed a typical time-dependent increase in the milk maturation parameter (MM%), characterized by an initial steep increase, followed by a moderate increase, and reaching a plateau during the first weeks postpartum. Additionally, MM% levels captured by the system were found to be sensitive to breastfeeding status classifications of exclusive breastfeeding and breastfeeding problems, manifested by differences in group means in the several-day range after birth, predominantly during the first weeks postpartum. Differences could also be demonstrated for the per-case time after birth-dependent progress in individual mothers.
CONCLUSIONS: This feasibility study demonstrates that the use of smart milk conductivity sensing technology can provide a robust, objective measure of individual breastfeeding efficiency, facilitating remote data collection within a home setting. This system holds considerable potential to augment both self-monitoring and remote breastfeeding management capabilities, as well as to refine clinical classifications. To further validate the clinical relevance and potential of this home milk monitoring tool, future controlled clinical studies are necessary, which will provide insights into its impact on user and care provider satisfaction and its potential to meet breastfeeding success goals.

Smart living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for smart living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in smart living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of smart living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in smart living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of context awareness, data availability, personalization, and privacy in HAR, this paper offers a comprehensive resource for researchers and practitioners striving to advance smart living services and applications. The methodology for this literature review involved conducting targeted Scopus queries to ensure a comprehensive coverage of relevant publications in the field. Efforts have been made to thoroughly evaluate the existing literature, identify research gaps, and propose future research directions. The comparative advantages of this review lie in its comprehensive coverage of the dimensions essential for smart living services and applications, addressing the limitations of previous reviews and offering valuable insights for researchers and practitioners in the field.

Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains, i.e., Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to further explore and advance the field of human action recognition in smart living.

Biosensors are analytical tools that can be used as simple, real-time, and effective devices in clinical diagnosis, food analysis, and environmental monitoring. Nanoscale functional materials possess unique properties such as a large surface-to-volume ratio, making them useful for biomedical diagnostic purposes. Nanoengineering has resulted in the increased use of nanoscale functional materials in biosensors. Various types of nanostructures i.e., 0D, 1D, 2D, and 3D, have been intensively employed to enhance biosensor selectivity, limit of detection, sensitivity, and speed of response time to display results. In particular, carbon nanotubes and nanofibers have been extensively employed in electrochemical biosensors, which have become an interdisciplinary frontier between material science and viral disease detection. This review provides an overview of the current research activities in nanofiber-based electrochemical biosensors for diagnostic purposes. The clinical applications of these nanobiosensors are also highlighted, along with a discussion of the future directions for these materials in diagnostics. The aim of this review is to stimulate a broader interest in developing nanofiber-based electrochemical biosensors and improving their applications in disease diagnosis. In this review, we summarize some of the most recent advances achieved in point of care (PoC) electrochemical biosensor applications, focusing on new materials and modifiers enabling biorecognition that have led to improved sensitivity, specificity, stability, and response time.