Search Results (1,009)

Digital telecommunications have become the backbone of modern healthcare, transforming how patients and professionals interact, share information, and deliver treatment. The integration of telecommunications with medicine, biomedical engineering and health services has enabled rapid growth in telemedicine, remote patient monitoring, wearable biomedical devices, and data-driven clinical decision-making. Emerging technologies such as artificial intelligence, big data analytics, virtual and augmented reality and robotic tele-surgery are further expanding the scope of digital health. This review provides a comprehensive overview of the role of telecommunications in medicine and biomedical engineering. We classify key applications, highlight enabling technologies and critically examine the challenges regarding interoperability, data security, latency, and cost. Finally, we discuss future directions, including 5G/6G networks, edge computing, and privacy-preserving medical AI, emphasizing the need for reliable and equitable access to telecommunications-enabled healthcare worldwide. Full article

Wearable technologies are increasingly integrated into digital health systems to support continuous remote monitoring in oncology; however, the lack of standardized and reproducible criteria for device selection limits their scalable and regulation-compliant adoption in clinically oriented infrastructures. This study proposes a preclinical benchmarking framework for the systematic evaluation of commercially available wearable devices for oncology applications. Devices were assessed across six predefined dimensions: biometric data acquisition, application programming interface-based interoperability, regulatory compliance, battery autonomy, cost, and absence of mandatory subscription fees. From an initial pool of 23 devices, a stepwise screening process identified 6 eligible wearables, which were compared using a semi-quantitative weighted scoring system. The benchmarking analysis identified the Withings ScanWatch 2 as the highest-ranked device, achieving a score of 37/40 and representing the only solution combining medical-grade certification for selected functions, extended battery life (up to 30 days), declared General Data Protection Regulation-compliant data governance, and fully accessible application programming interfaces. The remaining devices scored between 17 and 23 due to limitations in certification, battery autonomy, or data accessibility. This work introduces a reproducible preclinical benchmarking methodology that supports transparent wearable device selection in oncology and provides a foundation for future scalable digital health integration under appropriate regulatory and interoperability governance. Full article

Frailty in older adults is a multidimensional syndrome characterized by reduced physiological resilience and heightened vulnerability to adverse outcomes, yet conventional assessments remain largely clinic-based. This study evaluated the feasibility and explanatory utility of smartphone-based digital lifelogs for assessing frailty in community-dwelling older adults. In a prospective observational study, 300 participants (mean age 73.30, SD 5.37 years) from three sites in Seoul, South Korea, used a custom mobile application for two weeks that passively collected sensor-derived gait speed, 30 s sit-to-stand counts, and daily and hourly step counts, alongside self-reported ratings of perceived exertion and subjective health. Frailty Index (FI) scores were computed, and Pearson correlations, hierarchical linear regression, and independent linear regression were applied to examine associations and model explanatory performance. Significant correlations were observed between FI and gait speed, sit-to-stand performance, daily step counts, perceived exertion, and subjective health. Incorporating digital lifelogs significantly improved explained variance in frailty beyond clinical indicators (ΔR² = 0.183), with gait speed and daily step counts emerging as key predictors. A model including only digital lifelogs also significantly associated with frailty (R² = 0.288). These findings suggest that smartphone-based lifelogging offers a feasible, practical, and informative method for two-week monitoring and cross-sectional assessment in community settings. Full article

Agricultural mechanization and advanced technology have developed significantly in the coffee industry. However, there are still requirements for human laborers to operate, monitor crop health care, and manage production. The integration of advanced technology can significantly enhance the production efficiency and management practices of agricultural enterprises. This paper aims to address these gaps by proposing and implementing a computer vision-based attendance tracking system on mobile platforms that are suitable for the requirements and limitations of agricultural enterprises. First, the face detection process involves interpreting and locating facial structure. Next, the model transforms a photographic image of a human face into digital data based on the unique features and facial structure. We utilize the InsightFace model with the buffalo_l variant, as well as ArcFace with a ResNet backbone, as a facial recognition algorithm. After capturing a facial image, the system conducts a matching process against the existing database to verify identity. Finally, we implement a mobile application prototype on both iOS and Android platforms, ensuring accessibility for farm workers. As a result, our system achieved 95.2% accuracy on the query set, with an average processing time of <200 ms per image (including face detection, embedding extraction, and database matching). The system performs real-time attendance monitoring, automatically recording the entry and exit times of farm workers using facial recognition technology, and enables quick registration of new workers. Our work is expected to enhance transparency and fairness in the human management process, focusing on the coffee farm use case. Full article

Fishermen operating in pelagic fisheries often experience significant barriers to medical care due to geographic isolation, harsh environmental conditions, and the absence of onboard healthcare personnel. Telemedicine offers an effective approach to overcome these limitations by enabling remote diagnosis, monitoring, and treatment through satellite-based communication systems. This review summarizes the progress and applications of telemedicine in maritime and other austere environments, focusing on technological advancements, clinical implementations, and emerging trends in artificial intelligence-driven healthcare. Evidence from pilot and retrospective studies highlights the growing use of wearable devices, telementored ultrasound, digital photography, and cloud-based monitoring systems for managing acute and chronic medical conditions at sea. The integration of machine learning and deep learning algorithms has further improved fatigue, stress, and motion detection, enhancing early risk assessment among seafarers. Despite challenges such as limited connectivity, data privacy concerns, and training requirements, the adoption of telemedicine significantly improves health outcomes, reduces emergency evacuations, and promotes occupational safety. Future directions emphasize the development of 5G-enabled Internet of Medical Things networks and predictive AI tools to establish comprehensive maritime telehealth ecosystems for fishermen in pelagic operations. Full article

Intelligent driving cabins operated by artificial intelligence technology are evolving into the third living space. They aim to integrate perception, analysis, decision making, and intervention. By using multimodal biosignal acquisition technologies (flexible sensors and non-contact sensing), it is possible to monitor the physiological indicators of heart rate and blood pressure in real time. Leveraging the benefits of domain controllers in the vehicle and edge computing helps the AI platform reduce data latency and enhance real-time processing capabilities, as well as integrate the cabin’s internal and external data through machine learning. Its aim is to build tailored health baselines and high-precision risk prediction models (e.g., CNN, LSTM). This system can initiate multi-level interventions such as adjustments to the environment, health recommendations, and ADAS-assisted emergency parking with telemedicine help. Current issues consist of sensor precision, AI model interpretation, security of data privacy, and whom to attribute legal liability to. Future development will mainly focus on cognitive digital twin construction, L4/L5 autonomous driving integration, new biomedical sensor applications, and smart city medical ecosystems. Full article

Cardiovascular–Kidney–Metabolic (CKM) syndrome arises from interrelated cardiovascular, renal, and metabolic pathways that require coordinated therapeutic strategies. This narrative review synthesizes recent systematic reviews, meta-analyses, and original studies to evaluate the translational application of lifestyle medicine (LM) for CKM management. Evidence indicates that LM interventions targeting the six pillars of practice (nutrition, physical activity, stress management, sleep, social support, and avoidance of risky substances) can improve blood pressure, lipid profiles, glycemic control, and weight, with benefits that complement or at times rival pharmacotherapy. We outline opportunities at the LM–drug interface, including sodium-glucose cotransporter-2 inhibitors and nutrient-stimulated hormone agents such as GLP-1 and GIP, and highlight the need to test synergy and sequencing with LM. Persistent implementation barriers include prioritization of drug-centric care and limited protocolized delivery; the AHA 5A model and digital health tools, including wearables that enable real-time feedback, provide practical routes for integration. Marginalized populations carry a disproportionate burden of CKM, underscoring the need for equitable, culturally tailored approaches. Sex-specific gaps, particularly in post-menopausal lipid metabolism and insulin sensitivity, point to the promise of genomic and nutraceutical personalization. Future work should use preregistered, adequately powered multimodal trials to establish durable, scalable pathways for CKM care. Full article

Digital twin technology is emerging as a critical enabler for the lifecycle management of complex engineering equipment, yet its implementation faces significant hurdles. Generic, one-size-fits-all digital twin platforms often fail to address the unique characteristics of this domain—such as tightly coupled multi-physics, high-fidelity modeling requirements, and the need for real-time model execution under harsh operating conditions. This creates a critical need for a structured, reusable blueprint. However, a dedicated reference architecture that systematically guides the development of such specialized digital twins is notably absent. To bridge this gap, this paper proposes MetaD-DT, a reference architecture designed to enable and streamline the development of digital twins specifically for complex engineering equipment. We detail its comprehensive four-layer architecture, core functional modules, and streamlined graphical development workflow. The MetaD-DT’s efficacy and practical value are validated through two distinct industrial case studies: a health management system for diesel engine Diesel Particulate Filter (DPF) and an intelligent control optimization system for Indirect Air-Cooled (IAC) towers. These applications validate the framework’s ability to support the creation of robust digital twins that can effectively handle complex industrial dynamics and improve O&M (Operation And Maintenance) efficiency. This work provides a systematic architectural blueprint for the future development of specialized and efficient digital twins in the engineering equipment domain. Full article

Taiwan’s strategic focus in digital healthcare has been officially integrated into national industrial policy and identified as a crucial application area for artificial intelligence (AI) and next-generation communication technologies. As the healthcare sector undergoes rapid digital transformation, digital healthcare technologies have emerged as essential tools for improving medical quality and efficiency. Leveraging the extensive coverage of its National Health Insurance (NHI) system and its strengths in Information and Communications Technology (ICT), Taiwan also benefits from the robust research capacity of universities and hospitals. Government-driven regulatory reforms and infrastructure initiatives are further accelerating the advancement of the NHI MediCloud system and the broader digital healthcare ecosystem. This article provides a comprehensive overview of smart healthcare development, highlighting government policy support and the R&D capabilities of universities, research institutes, and hospitals. It also examines the ICT industry’s participation in the development of smart healthcare ecosystems, such as Foxconn, Quanta, Acer, ASUS, Wistron, Qisda, etc. With strong data assets, technological expertise, and policy backing, Taiwan demonstrates significant potential in both AI innovation and smart healthcare applications, steadily positioning itself as a key player in the global healthcare market. Full article

Digital twins (DTs) powered by artificial intelligence (AI) are becoming important transformational tools in healthcare, enabling real-time simulation and personalized decision support at the patient level. The aim of this review is to critically examine the evolution, current applications, and future potential of AI-based DTs in healthcare, with a particular focus on their role in enabling real-time simulation and personalized patient-level decision support. Specifically, the review aims to provide a comprehensive overview of how AI-based DTs are being developed and implemented in various clinical domains, identifying existing scientific and technical gaps and highlighting methodological, regulatory, and ethical issues. Taking a “local to global” perspective, the review aims to explore how individual patient-level models can be scaled and integrated to inform population health strategies, global data networks, and collaborative research ecosystems. This will provide a structured foundation for future research, clinical applications, and policy development in this rapidly evolving field. Locally, DTs allow medical professionals to model individual patient physiology, predict disease progression, and optimize treatment strategies. Hospitals are implementing AI-based DT platforms to simulate workflows, efficiently allocate resources, and improve patient safety. Generative AI further enhances these applications by creating synthetic patient data for training, filling gaps in incomplete records, and enabling privacy-respecting research. On a broader scale, regional health systems can use connected DTs to model population health trends and predict responses to public health interventions. On a national scale, governments and policymakers can use these insights for strategic planning, resource allocation, and increasing resilience to health crises. Internationally and globally, AI-based DTs can integrate diverse datasets across borders to support research collaboration and improve early pandemic detection. Generative AI contributes to global efforts by harmonizing heterogeneous data, creating standardized virtual patient cohorts, and supporting cross-cultural medical education. Combining local precision with global insights highlights DTs’ role as a bridge between personalized and global health. Despite the efforts of medical and technical specialists, ethical, regulatory, and data governance challenges remain crucial to ensuring responsible and equitable implementation worldwide. In conclusion, AI-based DTs represent a transformative paradigm, combining individual patient care with systemic and global health management. These perspectives highlight the potential of AI-based DTs to bridge precision medicine and public health, provided ethical, regulatory, and governance challenges are addressed responsibly. Full article

Introduction. The proliferation of immersive virtual reality (VR) technologies has transformed the way individuals interact with digital environments, offering unprecedented opportunities in fields ranging from entertainment and education to healthcare and mental health interventions. Immersive VR is increasingly being implemented in motor and cognitive programs in neurorehabilitation, where patient safety and treatment adherence are critical. Despite its relevance, the conceptualization and measurement of cybersickness (CS) remain fragmented across disciplines, with various assessment tools developed in isolation, targeting different symptom domains, populations, and use contexts. Aim. The aim of this systematic review is to identify, categorize, and critically appraise all existing instruments and scales developed to measure CS associated with immersive VR use. The secondary objectives involve examining the psychometric properties of the identified instruments to provide robust evidence for clinicians in assessing CS associated with VR, thereby supporting future scale development and standardization. Additionally, a further objective is to evaluate the specific applicability of these instruments and scales for measuring CS within neurorehabilitation settings, given the growing use of immersive VR in clinical practice with neurological populations. Methods. This systematic review was conducted in accordance with the Guideline for reporting systematic reviews of outcome measurement instruments (PRISMACOSMIN). The review protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO). Data extraction was performed independently by the two reviewers, and discrepancies were resolved by consensus or consultation with a third reviewer. To assess the psychometric robustness of existing CS assessment instruments used in virtual reality settings, we applied the methodology proposed by the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) initiative for systematic reviews of patient-reported outcome measures (PROMs). The evaluation was structured across three steps: Assessment of risk of bias; Quality of measurement properties and Summary of evidence; and Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidelines. Results. Nine assessment instruments were included in this review. Following our analysis, the CSQ-VR and VRNQ received a Grade A recommendation, as they met sufficient criteria for both internal consistency and structural validity with high methodological quality. Few instruments have reported validations in specific clinical populations related to neurorehabilitation, including individuals with neurological conditions such as brain injury, multiple sclerosis (MS), migraine-associated vestibulopathy, vestibular disorders or cognitive impairment, indicating a critical gap in scale generalizability across clinical contexts. Conclusions. Despite the increasing use of immersive VR, few CS assessment tools have been designed and validated, reaching the CSQ-VR and VRNQ a Grade A recommendation. Among the nine identified assessment instruments, only the SSQ, MSAQ, CSQ-VR, VRNQ MSSQ and SEQ have been employed in samples with neurological disorders. There is a critical need for standardized CS VR-specific tools with robust psychometric properties to ensure safe and effective implementation in neurorehabilitation settings. Full article

Virtual reality (VR) offers new opportunities for delivering psychologically meaningful conversations in digitally mediated settings. This study examined how environmental designs influence user experience during emotionally relevant self-disclosure. Fifty university students completed a within-subjects experiment in which they engaged in a structured positive and negative self-disclosure task across four immersive environments (seaside, garden, urban, and sci-fi). After each interaction, participants rated six experiential dimensions relevant to therapeutic communication: comfort, calmness, pleasantness, focus, privacy, and perceived overall suitability for psychological therapy. Repeated-measures analyses showed that nature-themed environments were rated more positively than non-nature environments across all dimensions. Although the seaside and garden environments did not differ in overall composite ratings, the seaside setting was most frequently preferred for comfort, calmness, and pleasantness in participants’ final rankings. These findings demonstrate that virtual environment design meaningfully shapes users’ emotional and interpersonal experience in VR, highlighting the value of nature-based environments for VR counselling systems and digital mental-health applications. Full article

The reassessment of structural capacity is critical to maintain the safety, serviceability, and sustainability of ageing civil engineering infrastructure. Structural Health Monitoring (SHM) allows in situ measurements to be incorporated into structural models, updating the performance and reliability estimation based on available information. Digital Twins can be used to capture the behaviour of the system in the real world as live data and make use of rich sensorial data flow from the structural system. However, the growing complexity of multi-domain models, as well as decision-making and stakeholder interactions, makes it necessary to implement a structured modelling framework. This paper proposes a Model-Based Systems Engineering (MBSE) framework that incorporates an MBSE layer to coordinate model dependencies, correlate important parameters, and enforce traceability between measurement data, probabilistic assessment, and decision-making. Illustrated with a prototype application to an idealised case study of a bridge, this paper describes how using MBSE as a scalable, adaptive, and comprehensive framework can help enable data-driven structural reassessment. The work illustrates that MBSE can be used in civil engineering processes across multi-disciplinary departments to benefit the system lifecycle over time and identifies areas of further research required before the approach can be adopted for large-scale, real-world infrastructure. Full article

Women in vulnerable living situations with schizophrenia face intersecting challenges, including migration-related trauma, caregiving burdens, and systemic barriers such as cultural dislocation, limited healthcare access, and stigma. These factors heighten vulnerability compared with men and contribute to delayed diagnoses, poor treatment adherence, and adverse outcomes. Advances in artificial intelligence (AI) and digital tools offer potential support, though they should be regarded as complementary rather than stand-alone solutions. This review synthesizes literature on gender-specific care for women with schizophrenia, examining clinical, social, and reproductive needs alongside the impact of migration and psychosocial adversity. Emerging models, including women-focused psychiatric units, perinatal services, and community therapeutic spaces, illustrate holistic approaches that integrate the medical, psychological, and social dimensions of care. Digital interventions, such as smartphone applications, mobile health tools, and digital participation strategies, are considered supportive extensions that offer opportunities to improve access, reduce costs, and enhance continuity of care. Despite this promise, digital tools remain under-validated for women in precarious contexts. Ethical challenges, including algorithmic bias, data privacy risks, and the exclusion of undocumented or marginalized groups, further constrain equitable implementation. This review aims to articulate conceptual linkages among gender, migration, and digital innovation in schizophrenia care, identifying thematic patterns, ethical tensions, and structural limitations in the existing literature. The synthesis provides a foundation for future hypothesis development and interdisciplinary research to advance inclusive and equity-driven mental health interventions. Full article

Background/Objectives: There is increased advocacy for the potential for digital applications (apps) and the Internet of Things (IoT) to improve women’s health. We conducted a systematic review to assess and synthesise the role of Apps and the IoT in improving the health of non-pregnant women. Methods: Six databases were searched from inception to 13 February 2023. We included randomised controlled trials that assessed the effects of various Apps and the IoT with regard to improving the health of non-pregnant women in high-income countries. Our primary outcomes were health status and well-being or quality of life, and we assessed behaviour change as the secondary outcome. Screening, data extraction, and quality assessment were performed in duplicate. Study quality was assessed using the Cochrane Risk of Bias 2.0 tool. Narrative methods were used to synthesise study outcomes. Results: The search retrieved 18,433 publications and seven publications from six studies met the inclusion criteria. Participants included overweight or obese women, postmenopausal women, or women with stage I–III breast cancer. Intervention types varied across included studies but broadly included wearable or sensor-based personal health tracking digital technologies. The most commonly assessed intervention effect was on behaviour change outcomes related to promoting physical activity. Interventions administered yielded positive effects on health outcomes and well-being or quality of life in one study each, while three of the four studies that assessed behaviour change reported significant positive effects. Most included studies had methodological concerns, while study designs and methodologies lacked comparability. Conclusions: Based on our findings, the use of apps and the IoT may be promising for facilitating behaviour change to promote physical activity. However, more evidence is needed to assess the effectiveness of the IoT for improving health status, well-being and quality of life among non-pregnant women. Full article