Search Results (272)

In response to the stress commonly experienced by young people in high-pressure daily environments, a music-based stress-relief interactive system was developed by integrating music-assisted care with emotion-sensing technology. The design principles of the system were established through a literature review on stress, music listening, emotion detection, and interactive devices. A prototype was created accordingly and refined through interviews with four experts and eleven users participating in a preliminary experiment. The system is grounded in a four-stage guided imagery and music framework, along with a static activity model focused on relaxation-based stress management. Emotion detection was achieved using a wearable EEG device (NeuroSky’s MindWave Mobile device) and a two-dimensional emotion model, and the emotional states were translated into visual representations using seasonal and weather metaphors. A formal experiment involving 52 users was conducted. The system was evaluated, and its effectiveness confirmed, through user interviews and questionnaire surveys, with statistical analysis conducted using SPSS 26 and AMOS 23. The findings reveal that: (1) integrating emotion sensing with music listening creates a novel and engaging interactive experience; (2) emotional states can be effectively visualized using nature-inspired metaphors, enhancing user immersion and understanding; and (3) the combination of music listening, guided imagery, and real-time emotional feedback successfully promotes emotional relaxation and increases self-awareness. Full article

The COVID-19 pandemic catalyzed the rapid adoption of remote patient monitoring (RPM) technologies such as telemedicine and wearable devices (WDs), significantly transforming healthcare delivery. Telemedicine made virtual consultations possible, reducing in-person visits and infection risks, particularly for the management of chronic diseases. Wearable devices enabled the real-time continuous monitoring of health that assisted in condition prediction and management, such as for COVID-19. This narrative review addresses these transformations by uniquely synthesizing findings from 13 diverse studies (sourced from PubMed and Google Scholar, 2020–2024) to analyze the parallel evolution of telemedicine and WDs as interconnected RPM components. It highlights the pandemic’s dual impact, as follows: accelerating RPM innovation and adoption while simultaneously unmasking systemic challenges such as inequities in access and a need for robust integration approaches; while telemedicine usage soared during the pandemic, consumption post-pandemic, as indicated by the reviewed studies, suggests continued barriers to adoption among older adults. Likewise, wearable devices demonstrated significant potential in early disease detection and long-term health management, with promising applications extending beyond COVID-19, including long COVID conditions. Addressing the identified challenges is crucial for healthcare providers and systems to fully embrace these technologies and this would improve efficiency and patient outcomes. Full article

MXenes, a class of two-dimensional transition metal carbides and nitrides, emerged as a promising material for next-generation energy storage and corresponding applications due to their unique combination of high electrical conductivity, tunable surface chemistry, and lamellar structure. This review highlights recent advances in MXene-based composites, focusing on their integration into electrode architectures for the development of supercapacitors, batteries, and multifunctional devices, including triboelectric nanogenerators. It serves as a comprehensive overview of the multifunctional capabilities of MXene-based composites and their role in advancing efficient, flexible, and sustainable energy and sensing technologies, outlining how MXene-based systems are poised to redefine multifunctional energy platforms. Electrochemical performance optimization strategies are discussed by considering surface functionalization, interlayer engineering, scalable synthesis techniques, and integration with advanced electrolytes, with particular attention paid to the development of hybrid supercapacitors, triboelectric nanogenerators (TENGs), and wearable sensors. These applications are favored due to improved charge storage capability, mechanical properties, and the multifunctionality of MXenes. Despite these aspects, challenges related to long-term stability, sustainable large-scale production, and environmental degradation must still be addressed. Emerging approaches such as three-dimensional self-assembly and artificial intelligence-assisted design are identified as key challenges for overcoming these issues. Full article

Total Knee Arthroplasty (TKA) is a well-established surgical intervention for the management of end-stage knee osteoarthritis. While the procedure is generally successful, postoperative rehabilitation remains a key determinant of long-term functional outcomes. Traditional rehabilitation protocols, particularly those requiring in-person clinical visits, often encounter limitations in accessibility, patient adherence, and personalization. In response, emerging sensor technologies have introduced innovative solutions to support and enhance recovery following TKA. This review provides a thematically organized synthesis of the current landscape and future directions of sensor-assisted rehabilitation in TKA. It examines four main categories of technologies: wearable sensors (e.g., IMUs, accelerometers, gyroscopes), smart implants, pressure-sensing systems, and mobile health (mHealth) platforms such as ReHub^® and BPMpathway. Evidence from recent randomized controlled trials and systematic reviews demonstrates their effectiveness in tracking mobility, monitoring range of motion (ROM), detecting gait anomalies, and delivering real-time feedback to both patients and clinicians. Despite these advances, several challenges persist, including measurement accuracy in unsupervised environments, the complexity of clinical data integration, and digital literacy gaps among older adults. Nevertheless, the integration of artificial intelligence (AI), predictive analytics, and remote rehabilitation tools is driving a shift toward more adaptive and individualized care models. This paper concludes that sensor-enhanced rehabilitation is no longer a future aspiration but an active transition toward a smarter, more accessible, and patient-centered paradigm in recovery after TKA. Full article

Reproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning (ML) algorithms, offer new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes the current research on AI-powered behavioral monitoring tools and proposes a conceptual model, ReproBehaviorNet, that maps age- and sex-specific behaviors to biological processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring, leading to earlier detection of reproductive events and greater breeding precision. However, the implementation of such technologies also presents challenges, including the need for high-quality data, a costly infrastructure, and technical expertise that may limit access for small-scale producers. Despite these barriers, AI-assisted behavioral phenotyping has the potential to improve genetic progress, animal welfare, and sustainability. Interdisciplinary collaboration and responsible innovation are essential to ensure the equitable and effective adoption of these technologies in diverse farming contexts. Full article

Biomimetic robotics and sensor technologies are reshaping the landscape of healthcare and rehabilitation. Despite significant progress across various domains, many areas within healthcare still demand further bio-inspired innovations. To advance this field effectively, it is essential to synthesize existing research, identify persistent knowledge gaps, and establish clear frameworks to guide future developments. This systematic review addresses these needs by analyzing 89 peer-reviewed sources retrieved from the Scopus database, focusing on the application of biomimetic robotics and sensing technologies in healthcare and rehabilitation contexts. The findings indicate a predominant focus on enhancing human mobility and support, with rehabilitative and assistive technologies comprising 61.8% of the reviewed literature. Additionally, 12.36% of the studies incorporate intelligent control systems and Artificial Intelligence (AI), reflecting a growing trend toward adaptive and autonomous solutions. Further technological advancements are demonstrated by research in bioengineering applications (13.48%) and innovations in soft robotics with smart actuation mechanisms (11.24%). The development of medical robots (7.87%) and wearable robotics, including exosuits (10.11%), underscores specific progress in clinical and patient-centered care. Moreover, the emergence of transdisciplinary approaches, present in 6.74% of the studies, highlights the increasing convergence of diverse fields in tackling complex healthcare challenges. By consolidating current research efforts, this review aims to provide a comprehensive overview of the state of the art, serving as a foundation for future investigations aimed at improving healthcare outcomes and enhancing quality of life. Full article

Our study explored the accuracy of Fitbit recorded step count during the use of an assistive device (two-wheeled walker and standard cane) at various body positions (wrists, hips, and ankles). Participants (n = 11) ambulated an obstacle course (50 m total, including turns and a step up/down) a total of three times (two-wheeled walker, standard cane, and a deviceless control trial). Fitbit generated step counts (at the wrists, hips, and ankles) were then compared to the individual’s “actual” step count captured through video analysis. During the cane trial and the deviceless trial, all positions were significantly correlated (r = 0.764 to 0.984; p < 0.006) with the actual step count. However, increased variability (demonstrated by increased limits of agreement) was observed when the Fitbit was worn on the wrist (compared to the hips or ankles). During the walker trial, the step count was significantly correlated to the actual step count at the ankle and hip positions (r = 0.669 to 0.888; p < 0.017) with an average error of 1.5%, while it was not statistically correlated at the wrist with a 31.2% average error. Our study suggests that Fitbits are a good predictor of actual step count, with the caveat that the location of the Fitbit should be considered if an assistive device (e.g., two-wheeled rolling walker and single-point cane) is being used. Full article

Lung cancer remains one of the main causes of cancer-related death globally and a significant global health concern. There is an urgent need for safer and more effective therapeutic alternatives despite notable progress in therapy; issues such as drug resistance, side effects, metastasis, and recurrence still affect patient outcome and quality of life. The aim of this review is to examine recent developments in the application of herbal-drug-loaded nanoparticles as a new strategy for treating lung cancer. A thorough examination of different drug delivery systems based on nanoparticles is provided, highlighting their function in improving the solubility, bioavailability, and targeted delivery of herbal compounds. In addition, the review evaluates the biomarkers used for targeted therapy and examines how new personalised treatment approaches like wearable electronic patches, robotics-assisted interventions, smartphone-enabled therapies, AI-driven diagnostics, and lung-on-a-chip technologies can be integrated to improve the accuracy and effectiveness of lung cancer treatment. In conclusion, the combination of personalised medicine and nanotechnology may lead to revolutionary changes in lung cancer treatment in the future. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

The development of wearable robotic exoskeletons has seen rapid progress in recent years, driven by the growing need for technologies that support motor rehabilitation, assist individuals with physical impairments, and enhance human capabilities in both clinical and everyday contexts. Within this field, elbow exoskeletons have emerged as a key focus due to the joint’s essential role in upper limb functionality and its frequent impairment following neurological injuries such as stroke. With increasing research activity, there is a strong interest in evaluating these systems not only from a technical perspective but also in terms of user comfort, adaptability, and clinical relevance. This review investigates recent advancements in elbow exoskeleton technology, evaluating their effectiveness and identifying key design challenges and limitations. Devices are categorized based on three main criteria: mechanical structure (rigid, soft, or hybrid), actuation method, and sensing technologies. Additionally, the review classifies systems by their supported range of motion, flexion–extension, supination–pronation, or both. Through a systematic analysis of these features, the paper highlights current design trends, common trade-offs, and research gaps, aiming to guide the development of more practical, effective, and accessible elbow exoskeletons. Full article

The development of exoskeletons aimed at enhancing the mobility and autonomy of people with disabilities marks a significant advance toward social and occupational inclusion, fostering greater independence and improved quality of life. However, their implementation poses multidisciplinary challenges, including technical issues, usability, cost, and user acceptance. This article synthesizes the main challenges, recent advancements, and future perspectives identified in scientific literature through a systematic review conducted under the PRISMA^® methodology. Forty-three high-impact publications indexed in SCOPUS, Web of Science, ScienceDirect, Taylor & Francis, IEEE Xplore, and PubMed were analyzed, showing an almost perfect inter-rater agreement (Cohen’s Kappa = 0.8390). The findings underscore the need to optimize control systems, reduce costs, and improve device adaptability. Artificial intelligence emerges as a key enabler to overcome these limitations, offering more efficient, affordable, and personalized solutions. This work provides an up-to-date overview of the field and outlines future directions for exoskeleton research and development, highlighting their transformative potential in the lives of people with disabilities. Full article

With the rapid development of Internet of Things (IoT) and bioscience technology, wearable smart devices are developing toward advanced trends such as flexibility, convenience and environmental-friendliness. Poly (p-styrenesulfonic acid) (PSS), as a common template and dispersant, is indispensable in the polymerization of conductive polymers. However, the doping amount of PSS has a significant effect on the electrical conductivity of the polymer. Herein, different molar quantities of PSS were used to assist the polymerization of 3,4-ethylenedioxythiophene (EDOT) monomer in a horseradish peroxidase/hydrogen peroxide (HRP/H₂O₂) low-temperature system to obtain conductive finishing solutions with more excellent electrical properties. Then, the polyester nonwoven fabric was immersed in the conductive finishing solution, and when the addition ratio of EDOT and PSS was 1:2, the conductive performance was optimal (3.27 KΩ cm⁻¹). Finally, the conductive fabric was assembled into a pressure sensor and a temperature sensor, which can transmit Morse code in the form of single-parameter (pressure response or temperature response) or collaboration. Overall, this research has great potential for production of poly(3,4-ethylenedioxythiophene) (PEDOT)-based composites and their applications in smart wearable device. Full article

Object-related accidents among older adults often result from inadequately designed furniture and fixtures that do not accommodate age-related changes. However, technologies for quantitatively capturing how furniture and fixtures assist the body in daily life remain limited. This study addresses this gap by introducing a portable, non-disruptive system that measures and visualizes how humans interact with environmental objects, particularly during transitional movements such as standing, turning, or reaching. The system integrates wearable force sensors, motion capture gloves, RGB-D cameras, and LiDAR-based environmental scanning to generate spatial maps of body-applied forces, overlaid onto point cloud representations of actual living environments. Through home-based experiments involving 13 older adults aged 69–86 across nine households, the system effectively identified object-specific support interactions with specific furniture (e.g., doorframes, shelves) and enabled a three-dimensional comparative analysis across different spaces, including living rooms, entryways, and bedrooms. The visualization captured essential spatial features—such as contact height and positional context—without altering the existing environment. This study presents a novel methodology for evaluating life environments from a life-centric perspective and offers insights for the inclusive design of everyday objects and spaces to support safe and independent aging in place. Full article

Current tomato harvesters rely primarily on external color as the sole indicator of ripeness. However, this approach often results in premature harvesting, leading to insufficient lycopene accumulation and a suboptimal nutritional content for human consumption. Such limitations are especially critical in controlled-environment agriculture (CEA) systems, where maximizing fruit quality and nutrient density is essential for both the yield and consumer health. To address that challenge, this study introduces a novel, multimodal harvest readiness framework tailored to nutrient film technology (NFT)-based smart farms. The proposed approach integrates plant-level stress diagnostics and fruit-level phenotyping using wearable biosensors, AI-assisted computer vision, and non-invasive physiological sensing. Key physiological markers—including the volatile organic compound (VOC) methanol, phytohormones salicylic acid (SA) and indole-3-acetic acid (IAA), and nutrients nitrate and ammonium concentrations—are combined with phenomic traits such as fruit color (a*), size, chlorophyll index (rGb), and water status. The innovation lies in a four-stage decision-making pipeline that filters physiologically stressed plants before selecting ripened fruits based on internal and external quality indicators. Experimental validation across four plant conditions (control, water-stressed, light-stressed, and wounded) demonstrated the efficacy of VOC and hormone sensors in identifying optimal harvest candidates. Additionally, the integration of low-cost electrochemical ion sensors provides scalable nutrient monitoring within NFT systems. This research delivers a robust, sensor-driven framework for autonomous, data-informed harvesting decisions in smart indoor agriculture. By fusing real-time physiological feedback with AI-enhanced phenotyping, the system advances precision harvest timing, improves fruit nutritional quality, and sets the foundation for resilient, feedback-controlled farming platforms suited to meeting global food security and sustainability demands. Full article

This study presents a systematic bibliometric review of wearable technologies aimed at vulnerable road user (VRU) safety, covering publications from 2000 to 2025. Guided by PRISMA procedures and a PICo-based search strategy, 58 records were extracted and analyzed in CiteSpace, yielding visualizations of collaboration networks, publication trajectories, and intellectual structures. The results indicate a clear evolution from single-purpose, stand-alone devices to integrated ecosystem solutions that address the needs of diverse VRU groups. Six dominant knowledge clusters emerged—street-crossing assistance, obstacle avoidance, human–computer interaction, cyclist safety, blind navigation, and smart glasses. Comparative analysis across pedestrians, cyclists and motorcyclists, and persons with disabilities shows three parallel transitions: single- to multisensory interfaces, reactive to predictive systems, and isolated devices to V2X-enabled ecosystems. Contemporary research emphasizes context-adaptive interfaces, seamless V2X integration, and user-centered design, and future work should focus on lightweight communication protocols, adaptive sensory algorithms, and personalized safety profiles. The review provides a consolidated knowledge map to inform researchers, practitioners, and policy-makers striving for inclusive and proactive road safety solutions. Full article