Search Results (541)

Globally, technologically integrated housing solutions are increasingly relevant in addressing the challenges of aging populations and sustainable urban development. Drawing on the Technology Acceptance Model (TAM), this research investigates how perceptions of usefulness, ease of use, and attitudes influence relocation intention to smart retirement villages (SRVs), while also examining any significant differences between the socio-demographic variables and such intention. A total of 305 individuals aged 55 and above participated in an online survey, with data analyzed using IBM SPSS Statistics version 27 and AMOS-SEM version 25. The findings reveal that elderly individuals of Chinese ethnicity, those who are married, and those aged between 66 and 70 are more inclined to relocate to SRVs. Attitude and perceived usefulness significantly predict relocation intention, while perceived ease of use exerts an indirect effect through usefulness. These results highlight the importance of integrating user-centered technological design with socio-cultural and demographic considerations in the development of age-friendly built environments. The study offers insights for urban planners, policymakers, and developers seeking to create inclusive and sustainable smart housing solutions for aging populations. 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

Fall detection is a critical task in healthcare monitoring systems, especially for elderly populations, for whom timely intervention can significantly reduce morbidity and mortality. This study proposes a privacy-preserving and scalable fall-detection framework that integrates federated learning (FL) with transfer learning (TL) to train deep learning models across decentralized data sources without compromising user privacy. The pipeline begins with data acquisition, in which annotated video-based fall-detection datasets formatted in YOLO are used to extract image crops of human subjects. These images are then preprocessed, resized, normalized, and relabeled into binary classes (fall vs. non-fall). A stratified 80/10/10 split ensures balanced training, validation, and testing. To simulate real-world federated environments, the training data is partitioned across multiple clients, each performing local training using pretrained CNN models including MobileNetV2, VGG16, EfficientNetB0, and ResNet50. Two FL topologies are implemented: a centralized server-coordinated scheme and a ring-based decentralized topology. During each round, only model weights are shared, and federated averaging (FedAvg) is applied for global aggregation. The models were trained using three random seeds to ensure result robustness and stability across varying data partitions. Among all configurations, decentralized MobileNetV2 achieved the best results, with a mean test accuracy of 0.9927, F1-score of 0.9917, and average training time of 111.17 s per round. These findings highlight the model’s strong generalization, low computational burden, and suitability for edge deployment. Future work will extend evaluation to external datasets and address issues such as client drift and adversarial robustness in federated environments. Full article

Background/Objectives: Cochlear implants (CIs) and hearing aids (HAs) have enhanced auditory rehabilitation in elderly individuals, yet limitations in musical perception and psychosocial integration persist. This systematic review aimed to evaluate the effects of music therapy (MT) on the quality of life (QoL), self-esteem, auditory perception, and cognition in older CI and HA users. Methods: A comprehensive search of PubMed was conducted up to March 2022 following PRISMA guidelines. Studies involving participants aged ≥ 60 years with CIs and/or HAs were included. Ten studies (n = 21,632) met eligibility criteria. Data were extracted and assessed using the Newcastle–Ottawa Scale. Results: MT led to improved sound quality, with HISQUI19 scores rising from 60.0 ± 21.8 to 74.2 ± 27.5. Early MT exposure was associated with significantly better MUMU outcomes (p = 0.02). Bilateral CI users showed enhanced stereo detection (52% to 86%), and CI + HA users achieved CNC scores exceeding 95%. Postlingual CI users outperformed prelingual peers in musical discrimination (9.81 vs. 3.48; p < 0.001). Long-term HA use was linked to better a QoL and reduced loneliness. Conclusions: While music therapy appears to support auditory and psychosocial functioning in hearing-impaired older adults, the absence of randomized controlled trials limits causal inference regarding its effects. These results support its integration into hearing rehabilitation strategies for older adults. Full article

In the context of global demographic aging, falls among the elderly remain a major public health concern, often leading to injury, hospitalization, and loss of autonomy. This study proposes a real-time fall detection system that combines a modern computer vision model, YOLOv11 with integrated pose estimation, and an Artificial Intelligence (AI)-based voice assistant designed to reduce false alarms and improve intervention efficiency and reliability. The system continuously monitors human posture via video input, detects fall events based on body dynamics and keypoint analysis, and initiates a voice-based interaction to assess the user’s condition. Depending on the user’s verbal response or the absence thereof, the system determines whether to trigger an emergency alert to caregivers or family members. All processing, including speech recognition and response generation, is performed locally to preserve user privacy and ensure low-latency performance. The approach is designed to support independent living for older adults. Evaluation of 200 simulated video sequences acquired by the development team demonstrated high precision and recall, along with a decrease in false positives when incorporating voice-based confirmation. In addition, the system was also evaluated on an external dataset to assess its robustness. Our results highlight the system’s reliability and scalability for real-world in-home elderly monitoring applications. Full article

Awareness of design when planning public urban spaces assumes particular importance through its mission to meet the diverse needs of the different segments within the community. The elderly is considered one of the pivotal segments, with their influence on strengthening social cohesion, fortifying values, and upholding traditions. On the other hand, such a segment demands special physical, behavioral, and mental requirements that would entail specific consideration in the design process of public common spaces. The study aimed to identify and evaluate the most relevant and important indicators pertaining to the most effective design of an age-friendly public space, with community parks taken as a particular case. The study relies on a mixed approach, combining desk research, expert views, the Delphi technique, and the Analytical Hierarchy Process to achieve that purpose. It resulted in a group of sourced, filtered, and evaluated indicators classified into Physical, Experiential, and Social/Emotional categories, which were evaluated by a mixed representative group of academics, practitioners, governmental officials, and end users, being the elderly or their caretakers. Focus was placed on the central park in the Dahiyat Al-Hussein suburb in Amman, Jordan, which was utilized as a contextual case through which a refined design framework was extracted. This framework serves as a potential base that can be expanded and adapted to create a more generalizable model. 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

With the increase in the global aging population, the demand for elderly-friendly game products is growing rapidly. To address existing limitations, particularly in user demand extraction and design parameter setting, this study proposed a design framework integrating the BTM–AHP–AD–TOPSIS methods. The goal was to accurately identify the core needs of elderly users and translate them into effective design solutions. User reviews of elderly-friendly game products were collected from e-commerce platforms using Python 3.8-based web scraping. The Biterm Topic Model (BTM) was employed to extract user needs from review texts. These needs were prioritized using the Analytic Hierarchy Process (AHP) and translated into specific design parameters through Axiomatic Design (AD). Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to comprehensively evaluate multiple design schemes and select the optimal solution. The results demonstrate that the proposed design path offers a holistic method for progressing from need extraction to design evaluation. It effectively overcomes previous limitations, including inefficient need extraction, limited scope, unclear need weighting, and unreasonable design parameters. This method enhances user acceptance and satisfaction while establishing rigorous design processes and scientific evaluation standards, making it well suited for developing elderly-friendly products. Full article

(1) Background: The provision of monofunctional or inadequately distributed services in urban park green spaces often constrains residents’ opportunities and diversity for outdoor activities, particularly limiting access and participation for specific age groups or activity preferences. However, functional nodes with temporal and spatial flexibility demonstrate high-quality characteristics of resilient and shared services through integrated development. Accurately identifying user demand provides a solid basis for optimizing the functional configuration of urban parks. (2) Methods: This study took the old city area of Zhengzhou, Henan Province, China, as a case study. By collecting and integrating various types of data, such as geographic spatial data, field investigation data, and behavioral observations, we developed a population demand quantification method and a modular analysis approach for park service functions. This framework enabled correlation analysis between diverse user needs and park services. The study further classified and combined park functions into modular units, quantifying their elastic and shared service capabilities—namely, the adaptive flexibility and shared utilization capacity of park services. Additionally, we established a demand-responsive evaluation system for identifying and diagnosing problem areas in park services based on multi-source data. (3) Results: The demand response index and diagnostic results indicate that the supply of fitness facilities—particularly equipment-based installations—is insufficient within the old urban district of Zhengzhou. Among the three user groups—children, young and middle-aged adults, and the elderly—the elderly population exhibited the lowest demand response index, revealing a significant gap in meeting their specific needs. (4) Conclusions: Based on the research findings, a three-tier optimization strategy is proposed: A. improve green space connectivity to expand the service coverage of parks; B. implement multifunctional overlay and coordinated integration in spatial design based on site characteristics and demand diagnostics; and C. increase the total supply of facilities to enhance spatial efficiency in parks. By integrating the demand assessment data and diagnostic results, this approach enabled a data-driven reorganization of service types and targeted allocation of resources within existing park infrastructure, offering a practical tool and reference for the planning of urban outdoor activity spaces. Full article

The objective of this study is to design, develop, and analyze a tri-wheeled trolley integrated with a motor that incorporates adjustable and foldable features. The purpose of a trolley is to allow users to easily transport items from one place to another. However, problems arise when transporting objects across challenging surfaces, such as up a flight of stairs, using a conventional cart. This innovation uses multiple engineering skills to determine and develop the best possible design for a stair-climbing trolley. A tri-wheel mechanism is integrated into its motorized design, meticulously engineered for adjustability, ensuring compatibility with a wide range of staircase dimensions. The designed trolley was constructed considering elements and processes such as a literature review, conceptual design, concept screening, concept scoring, 3D modelling, engineering design calculations, and simulations. The trolley was tested, and the measured pulling force data were compared with the theoretical calculations. A graph of the pulling force vs. load was plotted, in which both datasets showed similar increasing trends; hence, the designed trolley worked as expected. The development of this stair-climbing trolley can benefit people living in rural areas or low-cost buildings that are not equipped with elevators and can reduce injuries among the elderly. The designed stair-climbing trolley will not only minimize the user’s physical effort but also enhance safety. On top of that, the adjustable and foldable features of the stair-climbing trolley would benefit users living in areas with limited space. Full article

With the rapid advancement of urbanization in China, the demand for community-based elderly care facilities (CECFs) has been increasing. One pressing challenge is the question of how to provide CECFs that not only meet the health needs of the elderly but also make efficient use of limited urban land resources. This study addresses this issue by adopting an integrated multi-method research framework that combines multi-objective optimization (MOO) algorithms, Spearman rank correlation analysis, ensemble learning methods (Random Forest combined with SHapley Additive exPlanations (SHAP), where SHAP enhances the interpretability of ensemble models), and Self-Organizing Map (SOM) neural networks. This framework is employed to identify optimal building configurations and to examine how different architectural parameters influence key daylight performance indicators—Useful Daylight Illuminance (UDI) and Daylight Factor (DF). Results indicate that when UDI and DF meet the comfort thresholds for elderly users, the minimum building area can be controlled to as little as 351 m² and can achieve a balance between natural lighting and spatial efficiency. This ensures sufficient indoor daylight while mitigating excessive glare that could impair elderly vision. Significant correlations are observed between spatial form and daylight performance, with factors such as window-to-wall ratio (WWR) and wall thickness (WT) playing crucial roles. Specifically, wall thickness affects indoor daylight distribution by altering window depth and shading. Moreover, the ensemble learning models combined with SHAP analysis uncover nonlinear relationships between various architectural parameters and daylight performance. In addition, a decision support method based on SOM is proposed to replace the subjective decision-making process commonly found in traditional optimization frameworks. This method enables the visualization of a large Pareto solution set in a two-dimensional space, facilitating more informed and rational design decisions. Finally, the findings are translated into a set of practical design strategies for application in real-world projects. Full article

Introduction: Delirium is a common and debilitating clinical complication among ICU patients. Despite the prevalence of this condition, there are insufficient data to support or refute the routine use of atypical antipsychotics since the existing evidence remains sparse and inconclusive. The objective of the present study was to evaluate whether pre-ICU administration of the atypical antipsychotic olanzapine is associated with a differential time to delirium resolution relative to the control condition. Methods: In this emulated clinical trial, we utilized the MIMIC-IV v3.1 database, which contains deidentified health records from approximately 65,000 ICU patients, to derive a cohort of patients with a positive delirium screening within 24 h of ICU admission. We exluded patients who received any antipsychotic other than olanzapine prior to ICU admission. We performed propensity score matching using logistic regression and nearest-neighbor matching (1:1, caliper = 0.2) to balance covariates between the olanzapine and control groups. The primary outcome was time to delirium resolution, defined as the first negative delirium assessment. A Cox proportional hazards model, adjusted for multiple covariates and incorporating age as a time-dependent variable, was used to examine the association between olanzapine use and delirium resolution. Interaction terms were included to evaluate effect modification by age and gender. Results: A total of 5070 patients with a positive delirium screening within 24 h and no exposure to other antipsychotics met the eligibility criteria; 421 olanzapine users were matched to 421 controls using propensity score matching. Covariate balance was achieved (all standardized mean differences < 0.1), and no multicollinearity was detected (all VIFs < 2). Pre-ICU olanzapine use was associated with a 27% decrease in the likelihood of delirium resolution (HR = 0.73; 95% CI: 0.63–0.86; p < 0.001). A significant interaction with age indicated that the negative impact of olanzapine on delirium resolution increased with advancing age (HR = 1.0024 per unit of age × log(time), p = 0.023), translating to a 2.4% increase in the risk of prolonged delirium resolution for every 10-year increase in age per log(time). There was no modification of the association according to gender. Discussion: The negative effect of olanzapine on ICU delirium resolution, particularly among the elderly, presented in this study is in line with the results of our earlier study showing a negative effect (i.e., prolonged ICU stay) among patients receiving quetiapine relative to both control and haloperidol conditions. Distinctly strong anticholinergic effects of both olanzapine and quetiapine relative to the other antipsychotic agents may be driving the identified negative outcomes. Conclusions: Results of this emulated clinical trial do not support the use of olanzapine for the treatment of ICU delirium because the agent prolongs time to resolution of the condition. 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

Human activity recognition (HAR) using Wi-Fi-based sensing has emerged as a powerful, non-intrusive solution for monitoring human behavior in smart environments. Unlike wearable sensor systems that require user compliance, Wi-Fi channel state information (CSI) enables device-free recognition by capturing variations in signal propagation caused by human motion. This makes Wi-Fi sensing highly attractive for ambient healthcare, security, and elderly care applications. However, real-world deployment faces two major challenges: (1) significant cross-subject signal variability due to physical and behavioral differences among individuals, and (2) limited labeled data, which restricts model generalization. To address these sensor-related challenges, we propose TCN-MAML, a novel framework that integrates temporal convolutional networks (TCN) with model-agnostic meta-learning (MAML) for efficient cross-subject adaptation in data-scarce conditions. We evaluate our approach on a public Wi-Fi CSI dataset using a strict cross-subject protocol, where training and testing subjects do not overlap. The proposed TCN-MAML achieves 99.6% accuracy, demonstrating superior generalization and efficiency over baseline methods. Experimental results confirm the framework’s suitability for low-power, real-time HAR systems embedded in IoT sensor networks. 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