Search Results (775)

The integration of artificial intelligence (AI) and advanced digital workflows is revolutionising full-arch implant dentistry, particularly for geriatric patients with edentulous and atrophic arches, for whom achieving both prosthetic passivity and optimal aesthetic outcomes is critical. This narrative review evaluates current challenges in intraoral scanning accuracy—such as scan distortion, angular deviation, and cross-arch misalignment—and presents how innovations like the Medit SmartX AI-guided workflow and the Scan Ladder system can significantly enhance precision in implant position registration. These technologies mitigate stitching errors by using real-time scan body recognition and auxiliary geometric references, yielding mean RMS trueness values as low as 11–13 µm, comparable to dedicated photogrammetry systems. AI-driven prosthetic design further aligns implant-supported restorations with facial symmetry and smile aesthetics, prioritising predictable midline and occlusal plane control. Early clinical data indicate that such tools can reduce prosthetic misfits to under 20 µm and lower complication rates related to passive fit, while shortening scan times by up to 30% compared to conventional workflows. This is especially valuable for elderly individuals who may not tolerate multiple lengthy adjustments. Additionally, emerging AI applications in design automation, scan validation, and patient-specific workflow adaptation continue to evolve, supporting more efficient and personalised digital prosthodontics. In summary, AI-enhanced scanning and prosthetic workflows do not merely meet functional demands but also elevate aesthetic standards in complex full-arch rehabilitations. The synergy of AI and digital dentistry presents a transformative opportunity to consistently deliver superior precision, passivity, and facial harmony for edentulous implant patients. Full article

The rapid growth of mobile financial services provides significant opportunities for enhancing digital financial inclusion among older adults. However, elderly consumers often lag in adoption and sustained usage due to psychological barriers (e.g., technology anxiety) and factors related to prior experience and comfort with technology (e.g., technology familiarity). This study investigates how technology anxiety and technology familiarity influence elderly consumers’ continuance intention toward mobile banking, while examining age as a moderator by comparing younger older adults (aged 60–69) and older adults (aged 70+). Using data from an online survey of 488 elderly mobile banking users in South Korea, we conducted hierarchical regression analyses. The results show that technology anxiety negatively affects continuance intention, whereas technology familiarity positively enhances sustained usage. Moreover, age significantly moderated these relationships: adults aged 70+ were notably more sensitive to both technology anxiety and familiarity, highlighting distinct age-related psychological differences. These findings underscore the importance of targeted digital literacy initiatives, age-friendly fintech interfaces, and personalized support strategies. This study contributes to the fintech literature by integrating psychological dimensions into traditional technology adoption frameworks and emphasizing age-specific differences. Practically, fintech providers and policymakers should adopt tailored strategies to effectively address elderly consumers’ unique psychological needs, promoting sustained adoption and narrowing the digital divide in financial technology engagement. Full article

Japan’s 8.5 million vacant homes (Akiyas) represent a paradox of scarcity amid surplus: while rural depopulation leaves properties abandoned, housing shortages and bureaucratic inefficiencies hinder their reuse. This study proposes AKI2ALL, an AI-blockchain framework designed to automate the circular repurposing of Akiyas into ten high-value community assets—guesthouses, co-working spaces, pop-up retail and logistics hubs, urban farming hubs, disaster relief housing, parking lots, elderly daycare centers, exhibition spaces, places for food and beverages, and company offices—through smart contracts and data-driven workflows. By integrating circular economy principles with decentralized technology, AKI2ALL streamlines property transitions, tax validation, and administrative processes, reducing operational costs while preserving embodied carbon in existing structures. Municipalities list properties, owners select uses, and AI optimizes assignments based on real-time demand. This work bridges gaps in digital construction governance, proving that automating trust and accountability can transform systemic inefficiencies into opportunities for community-led, low-carbon regeneration, highlighting its potential as a scalable model for global vacant property reuse. Full article

The rapid rise of the silver economy in the new era has become a new driving force for socio-economic development. From the perspective of Marxist political economy theory, this paper analyzes the intrinsic logic of the silver economy’s development through three dimensions: surplus value, labor market, and capital. The study finds that the silver economy in the new era faces challenges such as insufficient supply of high-quality elderly care services, simultaneous shortages in both total talent quantity and structural imbalances, and contradictions between capital’s profit-seeking nature and social welfare. By introducing the multiple streams model, the paper elucidates the coupling process of these three streams and the timing of policy window openings. It proposes targeted strategies, including strengthening technological innovation, deepening labor market reforms, and optimizing capital allocation, to promote the robust development of China’s silver economy and inject strong momentum into sustainable and high-quality economic growth. Full article

This study addresses the problem of agricultural waste utilization and nutrition for older adults by developing a food product based on a circular design approach. Pineapple core was used to produce a clean-label dietary powder without chemical or enzymatic treatment, relying on repeated rinsing and hot-air drying. The development process followed a structured analysis of physical, chemical, and sensory properties. The powder contained 83.46 g/100 g dietary fiber, 0° Brix sugar, pH 4.72, low water activity (aw < 0.45), and no detectable heavy metals or microbial contamination. Sensory evaluation by expert panelists confirmed that the product was acceptable in appearance, aroma, and texture, particularly for older adults. These results demonstrate the feasibility and safety of valorizing agri-waste into functional ingredients. The process was guided by the Transformative Circular Product Blueprint, which integrates clean-label processing, IoT-enabled solar drying, and decentralized production. This model supports traceability, low energy use, and adaptation at the community scale. This study contributes to sustainable food innovation and aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation and Infrastructure), and 12 (Responsible Consumption and Production). Full article

This study aimed to investigate the effects of augmented reality (AR) treadmill walking training on cognitive function, body composition, physiological responses, and acceptance among older adults. Additionally, it analyzed the relationships between body composition, physiological responses, and the acceptance of AR technology. A randomized controlled trial was conducted, recruiting 60 healthy older adults, who were assigned to either the experimental group (AR treadmill walking training) or the control group (traditional treadmill walking training). The assessments included cognitive function evaluation (stride length, walking speed, and balance test), body composition (BMI, skeletal muscle mass, fat mass, and body fat percentage), and physiological responses (heart rate, calorie expenditure, exercise duration, and distance covered). Furthermore, the AR Acceptance Scale was used to assess perceived ease of use, perceived usefulness, attitudes, and behavioral intentions. The results indicated that AR treadmill walking training had significant positive effects on improving cognitive function, optimizing body composition, and enhancing physiological responses among older adults. Compared with the traditional training group, the experimental group demonstrated better performance in stride length, walking speed, and balance tests, with increased skeletal muscle mass and reduced body fat percentage. Additionally, improvements were observed in heart rate regulation, calorie expenditure, exercise duration, and distance covered, reflecting enhanced exercise tolerance. Moreover, older adults exhibited a high level of acceptance toward AR technology, particularly in terms of attitudes and behavioral intentions, as well as perceived usefulness. This study provides empirical support for the application of AR technology in promoting elderly health and suggests that future research should explore personalized adaptation strategies and long-term effects to further expand the potential value of AR technology in elderly exercise. Full article

Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications. Full article

Objectives: An aging population and declining birth rates are among the challenges that smart cities currently face and will continue to face in the near future. In light of the above, this article seeks to answer the following question: Are older people (seniors) taken into account and described in the literature on smart cities, and if so, how? Methods: To answer this research question, a systematic literature review was conducted using the Bibliometrix package in R. In the process of systematizing the publications, the authors additionally used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method and qualitative text analysis. Findings: The research shows that relatively little attention is paid to seniors in smart cities in the literature on the subject. Among the few publications on smart aging, the technological trend dominates, in which researchers present the possibilities of using IT and ICT to improve medical and social care for seniors, and to improve their quality of life (Smart Living, Smart Mobility). In the non-technological trend, most analyses focus on the determinants of quality of life and the distinguishing features of senior-friendly cities. Implications: There is a clear lack of a “human” perspective on aging in smart cities and publications on Smart Governance and Smart People that would provide guidelines for making elderly people full and equal stakeholders in smart cities. It is also necessary to develop practical documents and procedures that define a comprehensive and long-term urban policy for elderly adults. The analyses contribute to diagnosing current and determining further directions of research on smart aging in smart cities. The results clearly imply the need to intensify social, humanistic, and governance research on the role of seniors in smart cities. Full article

Technological advancements and AI-based research have significantly influenced our daily lives. Human activity recognition (HAR) is a key area at the intersection of various AI technologies and application domains. In this study, we present our novel time series classification approach for monitoring the physical behaviors of the elderly and patients. This approach, which integrates supervised and unsupervised methods with generative models, has been validated for HAR, showing promising results. Our method was specifically adapted for healthcare and surveillance applications, enhancing the classification of physical behaviors in the elderly. The hybrid approach proved its effectiveness on the HAR70+ dataset, surpassing traditional recurrent convolutional network-based approaches. We further evaluated the surveillance system for the elderly (Surv-Sys-Elderly) model on the HARTH and HAR70+ datasets, achieving an accuracy of 94,3% on the HAR70+ dataset for recognizing elderly behaviors, highlighting its robustness and suitability for both clinical and domestic environments. Full article

Background: In elderly community services, information technology is reshaping the daily lives of older adults in unprecedented ways. It effectively addresses the issue of frailty in the community by strengthening support networks and dynamic risk management. Despite its vast potential, there remains a need to explore further enabling methods in the realm of elderly community services. Objectives: This study aims to provide a significant theoretical and practical foundation for information technology in this field by systematically analyzing the progress and trends of digital transformation facilitated by information technology. Materials and method: To map the advancements and emerging trends in this evolving field, this study conducts a bibliometric analysis of 461 relevant publications from the Web of Science Core Collection (2004–2024). The research employs bibliometric methods and utilizes tools such as CiteSpace and VOSViewer to analyze collaborations, keywords, and citations, as well as to perform data visualization. Results: The findings indicate that current research hotspots mainly focus on “community care”, “access to care”, “technology”, and “older adults”.Potential development trends include (1) further exploration of information technology in elderly care to provide more precise health management solutions; (2) systematically building community elderly service systems to offer more detailed elderly care services; (3) strengthening interdisciplinary information sharing and research collaboration to drive innovation in community elderly care models; and (4) introducing targeted policy and financial support to improve the specific implementation framework of information technology in elderly community services. Conclusions: This study provides empirical support for the development of relevant theories and practices. Furthermore, the research outcomes offer valuable insights into business opportunities for practitioners and provide important recommendations for formulating elderly service policies. Full article

Visual impairment or even loss seriously affects quality of life. Benefited by the rapid development of sound/laser detection, Global Positioning System (GPS)/Beidou positioning, machine vision and other technologies, the quality of life of blind people is expected to be improved through visual substitution technology. The existing visual substitution devices still have limitations in terms of safety, robustness, and ease of operation. The remote companion system developed here fully utilizes multimodal navigation and remote communication technologies, and the positioning and interaction functions of commercial mobile phones. Together with the accumulated judgment of backend personnel, it can provide real-time, safe, and reliable navigation services for blind people, helping them complete daily activities such as independent travel, circulation, and shopping. The practical results show that the system not only has strong operability and is easy to use, but also can provide users with a strong sense of security and companionship, making it suitable for promotion. In the future, this system can also be promoted for other vulnerable groups such as the elderly. Full article

As the aging population grows, ensuring effective and sustainable health management for elderly individuals has become a critical challenge. This study explores the integration of smart healthcare technologies and ESG (Environmental, Social, and Governance) principles to enhance elderly health management through data-driven strategies. Using the MIMIC-III database, this study evaluates five machine learning models (Adaboost, Bagging, Catboost, GaussianNB, and SVC) through ten-fold cross-validation to predict 3-day and 30-day mortality rates among elderly ICU patients. The Bagging model achieved the best performance with an AUROC of 0.80, demonstrating the potential of smart healthcare in mortality prediction. These technologies enhance predictive accuracy, enabling the timely identification of high-risk patients and effective intervention. Through the application of smart data integration methods, this study demonstrates how combining clinical indicators with socioeconomic factors can improve healthcare equity and efficiency. Furthermore, by aligning smart healthcare development with ESG concepts, we emphasize the importance of sustainability, social responsibility, and governance transparency in future healthcare systems. The findings offer valuable contributions toward building an interoperable and ethical health ecosystem, supporting early risk identification, improved care outcomes, and the promotion of healthy living for the elderly population. Full article

The demographic shift toward an aging population calls for innovative strategies to ensure independence, health, and quality of life in later years. In this context, Ambient Assisted Living (AAL) solutions, supported by Information and Communication Technologies (ICTs), offer promising advances for non-invasive and continuous support. Commonly, ICTs are evaluated only from the perspectives related to key performance indicators (KPIs); nevertheless, the design and implementation of such technologies must account for important psychological, social, and ethical dimensions. Radar-based sensing systems are emerging as an option due to their unobtrusive nature and capacity to operate without direct user interaction. This work explores how radar technologies, particularly those operating in the millimeter wave (mmWave) spectrum, can provide core key value indicators (KVIs) essential to aging societies, such as human dignity, trustworthiness, fairness, and sustainability. Through a review of key application domains, the paper illustrates the practical contributions of mmWave radar in Ambient Assisting Living (AAL) contexts, underlining how its technical attributes align with the complex needs of elderly care environments and produce value for society. This work uniquely integrates key value indicator (KVI) frameworks with mmWave radar capabilities to address unmet ethical needs in the AAL domain. It advances existing literature by proposing a value-driven design approach that directly informs technical specifications, enabling the alignment of engineering choices with socially relevant values and supporting the development of technologies for a more inclusive and ethical society. Full article

As people age, more careful health monitoring becomes increasingly important. The article presents the development and implementation of an integrated system for monitoring the health of elderly individuals using Internet of Things (IoT) technology and a wearable bracelet to continuously collect vital data. The device integrates MAX30100 sensors for heart rate monitoring and MPU-6050 for step counting and sleep quality analysis (deep and superficial sleep). The collected data for average heart rate (AR), minimum (mR), maximum (MR), number of steps (S), deep sleep time (DST), and superficial sleep time (SST) is processed in real-time through a health anomaly detection algorithm (HADA), based on the dimensionality reduction method using PCA. The system is connected to the Azure cloud infrastructure, ensuring secure data transmission, preprocessing, and the automatic generation of alerts for prompt medical interventions. Studies conducted over two years demonstrated a sensitivity of 100% and an accuracy of 98.5%, with a tendency to generate additional alerts to avoid overlooking critical events. The results outline the importance of personalizing the analysis, adapting algorithms to individual characteristics, and the system’s potential to prevent medical complications and improve the quality of life for elderly individuals. Full article

Older adults make up about 12% of the public sector, primary care, and hospital use and represent a large proportion of the users of healthcare services. Older people are also more vulnerable to serious injury from unexpected falls due to tripping, slipping, or illness. This underscores the immediate necessity of stable and cost-effective e-health technologies in maintaining independent living. Artificial intelligence (AI) and machine learning (ML) offer promising solutions for early fall prediction and continuous health monitoring. This paper introduces a novel cooperative AI model that forecasts the risk of future falls in the elderly based on behavioral and health abnormalities. Two AI models’ predictions are combined to produce accurate predictions: The ${AI}_1$ model is based on vital signs using Fuzzy Logic, and the ${AI}_2$ model is based on Activities of Daily Living (ADLs) using a Deep Belief Network (DBN). A meta-model then combines the outputs to generate a total fall risk prediction. The results show 85.71% sensitivity, 100% specificity, and 90.00% prediction accuracy when compared to the Morse Falls Scale (MFS). This emphasizes how deep learning-based cooperative systems can improve well-being for older adults living alone, facilitate more precise fall risk assessment, and improve preventive care. Full article