Search Results (478)

Driven by the profound convergence of the Internet of Things (IoT) and ubiquitous computing, wearable multifunctional sensors have emerged as a key technology for high-precision human activity recognition (HAR). Advancements in novel materials and flexible electronics have propelled the evolution of these sensors, enabling advances in decoupling heterogeneous signals, enhancing system robustness, and expanding environmental perception. This review systematically examines the frontier research on wearable multifunctional sensors for HAR. We provide an in-depth analysis of three core architectural design paradigms: architecture-level integration, which relies on physical spatial isolation for hardware-level signal decoupling; monolithic integration, which strives for extreme spatial compactness and spatiotemporal signal consistency; and the emerging intrinsically multifunctional design, which leverages novel stimuli-responsive materials for the intrinsic orthogonal discrimination of multidimensional signals. Furthermore, we delineate the diverse application scenarios of these highly integrated sensing platforms across medical rehabilitation, sports science, human–computer interaction (HCI), and daily behavior perception. Finally, this article discusses the critical challenges currently confronting this technology and outlines its future development prospects. Full article

Soft robotics is a rapidly evolving field that has attracted significant attention within the scientific community. This review analyzes the main advantages of pneumatic technology in service robots across the different application domains defined by the International Federation of Robotics (IFR). By organizing the literature according to application domains, this work aims to clarify the specific benefits of pneumatic and soft pneumatic solutions in each context. The proposed approach distinguishes between traditional pneumatic solutions and the subsequent emergence of soft robotics, in order to highlight how and to what extent soft technologies have reshaped the design and application scenarios. Particular attention is devoted to the role of materials and recent manufacturing techniques used by researchers to fabricate soft pneumatic robots. Based on 163 selected papers, the analysis reveals that medical and agricultural applications dominate soft pneumatic research, accounting for 41% and 27% of the soft sample, respectively. Compared to traditional pneumatics, the medical sector has expanded into cardiac assistive devices, wearable monitoring sensors, and minimally invasive surgery; agriculture has grown from 17% to 27% of the soft literature due to precision harvesting grippers. Soft inspection robots have increased thanks to continuum manipulators and bio-inspired locomotion, while search and rescue remains a niche (9%) but promising sector. Unlike previous reviews that focus on single domains or technologies, this work quantifies the uneven transition from rigid to soft pneumatics across IFR sectors and highlights emerging application-specific design paradigms that were not feasible with traditional systems. Full article

Currently, there is a widespread use of inertial motion units (IMUs) based on micromechanical systems (MEMS) with applications ranging from consumer electronics to medical devices. One of the main uses of this technology is in human body motion capture systems, which require attaching various IMUs to the body. It is customary to start the design of IMU-based motion capture solutions by using generic off-the-shelf (OTS) solutions or custom integrations. However, it is common that generic OTS solutions or custom IMUs integrations are not necessarily intended or designed to be directly attached to the human body. To address this issue, a widely adopted solution is to perform quick workarounds to enable the IMUs to be “worn” by prospective users. However, this can have the drawbacks of increased probability of detachment, improper fit, user discomfort, adding noise to the IMU measurements, etc. Therefore, the development of OTS IMU-based motion capture solutions would greatly benefit from having a methodological approach for the design of device housings and/or adaptations for OTS solutions or custom IMU integrations, such that they can be effectively used as wearable devices. In this work, we introduce a design methodology for wearable devices based on the Theory of Inventive Problem Solving (TRIZ). By designing a “wearable device housing” for an OTS IMU solution, we show that the proposed TRIZ-based methodology provides a straightforward and structured approach for the design of wearable devices. Furthermore, we illustrate how various challenges encountered in the early stages of prototype development can be effectively addressed using this methodology. The results obtained with the study case confirm that the proposed TRIZ-based methodology effectively overcomes the challenges associated with the design of wearable devices based on generic OTS solutions or custom IMU integrations. Full article

Purpose: This study explores how older adults with chronic back pain (CBP) evaluate different user interface (UI) designs and gamification elements for an ultrasound-based wearable providing real-time biofeedback during segmental stabilization exercises (SSE). The aim is to identify design preferences and motivational factors to enhance usability, engagement, and adherence in this specific population. Methods: We conducted a mixed-methods study with 15 older adults (aged ≥ 65) experiencing CBP. Participants interacted with three UI mockups (simple, anatomical, and playful) via a Wizard-of-Oz simulation and evaluated additional motivational elements (e.g., points, badges, progress charts). Semi-structured interviews and the Technology Usage Inventory (TUI) subscales were used to assess usability, acceptance, and intention to use. Results: Participants preferred the simple and anatomical UI designs, citing clarity, professionalism, and ease of interpretation. The playful design was viewed as less appropriate due to perceived infantilization. Game elements such as progress tracking, points, and levels were positively received, while competitive features like leaderboards were viewed critically. Most participants expressed interest in integrating pain education, favoring multimedia formats. Conclusions: Digital health tools for older adults must prioritize intuitive, medically reliable interfaces and allow personalization of motivational and educational components. The findings highlight the need for age-appropriate UI design and suggest that well-balanced gamification and educational features may enhance perceived acceptance and have the potential to support long-term use, which should be evaluated in longitudinal studies. Full article

Biomechanics sits at the interface of engineering and medical sciences, offering essential insight into how tissues, organs, and biological systems respond to mechanical loading. This review brings together recent advances in musculoskeletal and cardiovascular biomechanics, illustrating how experimental techniques, computational modeling, and multiscale analysis are used to characterize load transfer, tissue deformation, fatigue, and injury mechanisms. In musculoskeletal applications, predictive simulations, wearable sensing technologies, and neuromechanical assessment tools support improved injury prevention, rehabilitation planning, and assistive device development. In the cardiovascular domain, patient-specific modeling, fluid–structure interaction analyses, and advanced imaging approaches clarify how hemodynamics, vessel wall mechanics, and device–tissue interactions influence disease progression, implant performance, and therapeutic outcomes. Emerging technologies including artificial intelligence, machine learning, digital twin frameworks, biofabrication, soft robotics, and self-powered sensing are enabling data-driven, real-time, and personalized interventions that connect mechanistic understanding with clinical practice. Despite these advances, challenges remain in accounting for individual variability, integrating multiscale data, and translating computational predictions into clinically validated solutions. By emphasizing interdisciplinary strategies that unite biomechanics, computational analytics, and innovative device engineering, this review outlines a pathway toward predictive, patient-centered healthcare and next-generation therapeutic and rehabilitation solutions. Full article

Artificial intelligence (AI) is reshaping medical rehabilitation by advancing from isolated assistive technologies toward data-driven program management. Beyond established applications in robotics and virtual reality, AI enables multimodal data integration, predictive analytics, adaptive therapy optimization, and real-time monitoring across rehabilitation domains. This review synthesizes 61 peer-reviewed studies to examine how AI supports the management, planning, and evaluation of rehabilitation programs. The evidence indicates strong technical maturity at the device and session levels, particularly in robotic control and wearable monitoring, whereas longitudinal program orchestration and system-level coordination remain at an emerging stage. Machine learning, reinforcement learning, computer vision, and time-series models facilitate patient phenotyping, therapy personalization, and prognostic modeling. However, their scalability is constrained by limited interoperability, heterogeneous outcome measures, and insufficient multicenter validation. A structured six-layer management architecture is proposed to conceptualize AI as an integrated orchestration framework. Advancing toward scalable and trustworthy rehabilitation ecosystems will require interoperable infrastructures, longitudinal validation, and embedded ethical and explainability mechanisms. Full article

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

Flexible heat exchangers with intricate three-dimensional (3D) geometries exhibit superior mechanical and thermal performance compared with traditional two-dimensional (2D) designs. Their ability to offer greater design freedom and unique functionalities makes them particularly attractive for wearable medical devices. This study investigates flexible heat exchanger technologies in three main directions: (i) miniaturisation, (ii) integration of physical and mathematical models, and (iii) enhanced adaptability through heterogeneous design integration. Through a combination of literature review, mathematical modelling, and experimental analysis, the thermal efficiency of several configurations is compared, including basic thermoplastic polyurethane (TPU) tubes and 3D bio-inspired TPU tubes with aluminium-finned structures. The findings establish a foundation for the development of next-generation flexible wearable medical cooling devices with improved thermal management capabilities and practical applicability in industrial design. Furthermore, the outcomes of this research will directly support the development of improved wearable cooling devices within a UK-based medical device SME, Paxman Scalp Coolers, facilitating the translation of advanced heat exchanger designs into clinically relevant and commercially viable solutions. Full article

Background/Objectives: The rapidly expanding landscape of digital technologies is transforming innovation processes across industries, and the food sector is increasingly encouraged to adopt novel tools that can enhance development workflows and support competitive positioning. In the context of Industry 4.0, it is particularly important to examine open innovation approaches that may increase the efficiency of engineers and researchers involved in the research and development of food supplements. Such approaches enable broader access to relevant scientific information, including new bioactive ingredient research and their physiological implications, potentially contributing to the development of better-informed and higher-quality products. Methods: In the present study, we evaluated the deep research capabilities of several popular large language models to assess their suitability for supporting the conceptual design of a blood glucose-optimizing food supplement intended for prediabetes management. The comparative analysis focused on the level of detail in the outputs generated by each model, the robustness of the conclusions drawn, and the capacity to produce formulation-oriented recommendations grounded in scientific literature and regulatory frameworks. Our evaluation was primarily qualitative and subjective, highlighting both the potential and limitations of these models. Moreover, the study outlines a forward-looking concept for product validation using wearable smart devices and medically certified wearable devices with continuous biometric monitoring, which could provide an innovative avenue for assessing supplement efficacy. Results: The findings indicate that large language models can support the collection, organization, and preliminary interpretation of complex scientific information. Conclusions: Nevertheless, expert input remains essential for accurate evaluation, scientific validation, and regulatory compliance, as these models cannot yet replace domain expertise or rigorous experimentation in food supplement development. Full article

The management of complex acute and chronic wounds remains a formidable challenge in modern medicine, underscoring the urgent need for advanced therapeutic strategies that accelerate healing, prevent infection, and promote functional tissue regeneration. Electrospun nanofibers have attracted considerable attention in the biomedical field due to their extracellular matrix-like architecture, high surface area, interconnected porosity, and tunable physicochemical composition, which drive advances in wound regeneration, tissue engineering, and biopolymer-based therapeutics. In wound healing, nanofibrous dressings composed of natural polymers such as chitosan, gelatin, collagen, and cellulose promote cell attachment and proliferation, support angiogenesis, and enable infection control while delivering bioactive agents, thereby addressing significant challenges related to inflammation, biocompatibility, and antimicrobial resistance. In tissue engineering, aligned and hierarchically organized scaffolds fabricated from biopolymers such as collagen, gelatin, chitosan, and cellulose enhance the guided orientation of cells, differentiation, and functional regeneration of neural, musculoskeletal, vascular, and skin tissues. In addition to their conventional regenerative applications, recent studies have demonstrated that electrospun biopolymer nanofibers can be used in multifunctional biomedical platforms, including smart and stimuli-responsive systems for drug delivery, biosensing, regenerative interfaces, and wearable medical technologies. The integrated constructs that incorporate diagnostic or therapeutic functionalities, hybrid fabrication approaches that combine 3D printing with electrospinning, and intelligent biopolymer frameworks that enable telemedicine, real-time physiological monitoring, and personalized regenerative therapies offer new opportunities for developing improved biomedical systems. Overall, these advances position electrospun nanofiber systems as promising biomaterials for next-generation biomedical innovation. This review summarizes recent progress in tissue-engineered scaffolds, wound dressings, fabrication strategies for integrative therapeutics, and wearable devices with transformative potential for biomedical applications. Finally, the review addresses significant challenges related to scalability and clinical translation. It offers perspectives on future directions, including the integration of artificial intelligence and the regeneration of complex skin appendages, which will shape the next generation of nanofiber-based wound-healing therapies. Full article

Flexible X-ray detectors have emerged as a promising technology for portable medical imaging and wearable electronics, yet their manufacturing remains constrained by the competing requirements of device performance, mechanical conformability, and production scalability. Conventional solution-based deposition methods fail to yield high-quality perovskite thick films with uniform morphology, while vacuum evaporation techniques are limited by exorbitant operational costs and low throughput. Herein, we report an optimized solution-processed vacuum evaporation strategy that enables the fabrication of high-quality perovskite films (~1 μm thick) on flexible polyethylene naphthalate (PEN) substrates at a low processing temperature of 100 °C. By incorporating tailored additives into the precursor solution and precisely modulating the vapor-phase conversion kinetics, we achieved significant improvements in film density, crystallinity, and morphological uniformity. Systematic investigations were conducted to elucidate the structure–property relationships across three material systems: pure methylammonium lead iodide (MAPbI₃), halogen-doped methylammonium lead iodide-bromide (MAPb(IBr)₃), and synergistic cation-halogen engineered cesium-methylammonium lead iodide-bromide (CsMAPb(IBr)₃). The optimized flexible PIN-type X-ray detector based on CsMAPb(IBr)₃ exhibited exceptional performance metrics, including a dark current density as low as 5.2 nA cm⁻² and an X-ray sensitivity of up to 1.43 × 10⁴ μC·Gy_air⁻¹·cm⁻². Remarkably, the device retained over 95% of its initial performance after 400 bending cycles with a bending radius of 6 mm, demonstrating outstanding mechanical robustness and operational durability. This work establishes a viable, cost-effective technical route for the scalable production of high-performance flexible X-ray detectors, addressing critical challenges in the advancement of next-generation portable imaging technologies. Full article

Background: Chronic kidney disease (CKD) and end-stage renal disease (ESRD) represent a growing global health burden, affecting nearly one in ten adults worldwide. CKD is associated with high morbidity, premature mortality, reduced quality of life and enormous healthcare costs, and is primarily driven by dialysis and kidney transplantation. The silent and progressive nature of CKD means that most patients are diagnosed late, when irreversible damage has already occurred and costly kidney replacement therapies (KRT) become necessary. Dialysis services are resource-intensive, requiring significant infrastructure, specialized staff, and consumables, which makes them especially challenging to sustain in low- and middle-income countries. Traditional models of nephrology, care center-based dialysis and fragmented follow-up are increasingly inadequate in meeting the demands of a rising CKD population. These challenges highlight the urgent need for innovative approaches that enhance efficiency, improve patient outcomes, and expand access. Objective: This review aims to analyze the current landscape of information and communication technology (ICT) applications in nephrology and to evaluate how digital innovations are reconfiguring kidney therapy. Specifically, it seeks to identify the major ICT tools that are currently in use, assess their clinical and operational impact, and discuss their role in creating more sustainable, patient-centered kidney care models. This study reviews and analyzes ICT tools that are reconfiguring nephrology, including remote monitoring, AI, wearables, patient engagement apps and data dashboards. Methods: Narrative and scoping review of recent innovations in nephrology, including remote patient monitoring (RPM), telehealth, artificial intelligence (AI) analytics, wearable sensors, and clinical decision support platforms. Results: ICT tools such as Sharesource, Versia, telenephrology platforms, medical assistant for Chronic Care Service (MACCS), AI-based predictive analytics, wearable devices and patient engagement apps have improved patient outcomes, adherence, and early detection of complications. Key metrics include technique survival, hospitalization rate, patient-reported outcomes, workflow efficiency, and prediction accuracy. The relevant literature describing the potential of digital health technologies, including ICT platforms, artificial intelligence tools, and remote monitoring systems, to transform nephrology care was retrieved and screened for inclusion in this narrative review. Conclusions: ICT has shifted nephrology from reactive to proactive care, enhancing accessibility, patient empowerment and clinical efficiency. Future directions include precision nephrology, fully wearable kidneys, AI integration and large language models for education and triage. Challenges include digital divide, regulatory heterogeneity, cost and the need for long-term evidence. Full article

Textile materials represent a versatile class of engineering substrates widely used in apparel, domestic products, and medical protective systems. Despite their extensive application, large-scale textile production has seen limited integration of fundamentally new functionalization strategies. In recent years, however, advances in materials science have enabled the development of textiles with tailored electrical, adaptive, and biological functionalities. This review summarizes recent progress in the functionalization of textile materials with a focus on approaches relevant to engineering and industrial implementation. Particular attention is given to conductive textiles designed for operation under extreme environmental conditions, including low-temperature climates. Methods for integrating electrically conductive elements into fibrous structures are discussed, highlighting their potential for sensing, thermal regulation, and energy-related applications such as powering portable electronic devices. Inkjet printing is presented as a scalable technique for high-resolution deposition of conductive patterns while preserving the mechanical integrity and aesthetic properties of textile substrates. In addition, adaptive and stimuli-responsive textile systems are reviewed, including materials capable of responding to thermal, optical, or chemical stimuli, with applications in camouflage, wearable systems, and multifunctional surfaces. The review further addresses the development of bioactive textiles, emphasizing antibacterial functionalization using organic and inorganic agents to mitigate the spread of pathogenic microorganisms. The relevance of such materials has been underscored by recent global viral outbreaks. Overall, this work aims to provide a materials science perspective on emerging textile functionalization strategies and to facilitate the transition of these technologies from laboratory-scale research to practical engineering applications. Full article

Non-invasive monitoring technologies are increasingly being explored to support cancer care, yet most existing approaches focus on isolated parameters and fail to provide a comprehensive view of patients’ health. This study presents a feasibility evaluation of an IoT-based system designed to detect treatment-related problems in oncology patients through the integration of wearable sensors, physiological measurements, and patient-reported outcomes. A monitoring kit, including a smartwatch, tensiometer, weighing scale, and mobile device, was deployed in a cohort of 26 patients undergoing oncological treatment. Data acquisition followed a structured schedule: continuous physiological recording via the smartwatch, daily blood pressure measurements, weekly weight monitoring, and structured surveys capturing treatment-related side effects. These heterogeneous data were transformed into binary clinical metrics using rule-based feature extraction algorithms, covering conditions such as insomnia, nausea, diarrhea, abdominal pain, headache, weight loss, hypertension, and fever. Clinical specialists labeled the dataset to ensure medical validity. Machine Learning models were then trained to analyze the features and generate alerts for potential treatment complications. The results demonstrate the feasibility of integrating IoT and Artificial Intelligence techniques for continuous, patient-centered monitoring in oncology, paving the way for intelligent decision-support systems that enhance early detection and clinical management. Full article

Background: Technological innovation increasingly shapes modern sport, influencing performance, athlete safety, and regulatory governance. While new technologies enhance training and monitoring, they also raise concerns regarding fairness, health protection, and ethical legitimacy, commonly described as technological doping. The fragmented nature of the literature in this field requires integrative synthesis. Methods: A structured narrative synthesis was conducted using systematic searches and predefined eligibility criteria to identify studies addressing performance technologies, digital monitoring and detection systems, healthcare compliance, and governance and ethical frameworks. Twenty-four studies spanning empirical, policy, and conceptual domains were included. Results: Mechanical technologies, particularly advanced carbon-plate footwear, were associated with approximately 1–3% faster marathon performances and measurable alterations in lower-limb kinematics and kinetics under fatigue, while running-specific prostheses demonstrated performance-relevant differences in stiffness and energy return properties. Wearable monitoring systems supported training optimization but raised concerns related to surveillance and athlete autonomy. Artificial intelligence-based medication screening tools demonstrated high operational performance, with reported recognition accuracy ranging from approximately 92% to 98%, sensitivity approaching 1.00, and strong specificity for identifying prohibited substances from prescription images. Healthcare studies identified persistent knowledge gaps, medication risks, and the importance of pharmacists and education programs. Governance analyses revealed disparities in laboratory capacity and regulatory ambiguity when addressing emerging technologies, while ethical scholarship questioned the boundaries of legitimate enhancement. Conclusions: Technological doping reflects an interconnected performance–health–governance challenge rather than an isolated equipment issue. The synthesis demonstrates that technological doping is driven by measurable performance gains, digitally mediated compliance systems, and uneven regulatory capacity, indicating that future governance must shift from reactive equipment bans toward integrated, evidence-based oversight of biomechanical, digital, and healthcare technologies. Full article