Search Results (259)

This study investigates the impact of heatwaves on the thermal comfort and well-being of elderly individuals living alone during heatwaves, focusing on two contrasting residential typologies in the Atlantic climate of Spain: a dense urban area and low-density peri-urban setting. A mixed-methods approach was used, combining in situ environmental monitoring, adaptive comfort modelling, and user-centred data from surveys and interviews based on the De Jong-Gierveld Loneliness Scale. The results show that both dwellings exceeded recommended indoor temperature thresholds during heatwaves, especially at night, contributing to sleep disturbance, cardiovascular stress, and emotional discomfort. Despite 85% of participants indicating that outdoor activities help them to mitigate not-wanted loneliness, architectural barriers often hinder such engagement. Over half reported having no balcony or terrace, which may have further intensified social isolation. Field data collected during 2022 summer heatwaves recorded maximum daytime temperatures of 30 °C and night-time peaks of 28.7 °C, exceeding the 25 °C threshold. The adaptive comfort evaluation classified both cases as Class 4 (severe discomfort). The urban dwelling showed consistent moderate discomfort (Category 3), likely due to poor ventilation and urban heat island effects. The peri-urban case, despite lacking the heat island influence, showed worse thermal conditions, especially during the day. Architectural barriers, poor thermal performance, and the lack of semi-outdoor spaces may exacerbate isolation among elderly people during extreme heat events. 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

The challenges and increasing number of elderly individuals requiring remote monitoring at home highlight the need for technological innovations. This study devised an eHealth mobile application designed to detect abnormal movement behavior and alert caregivers when a lack of movement is detected for an abnormal period. By utilizing the built-in accelerometer of a conventional mobile phone, an application was developed to accurately record movement patterns and identify active and idle states. Fuzzy logic, an artificial intelligence (AI)-inspired paradigm particularly effective for real-time reasoning under uncertainty, was integrated to analyze activity data and generate timely alerts, ensuring rapid response in emergencies. The approach reduced development costs while leveraging the widespread familiarity with mobile phones, facilitating easy adoption. The approach involved collecting real-time accelerometry data, analyzing movement patterns using fuzzy logic-based inferencing, and implementing a rule-based decision system to classify user activity and detect inactivity. This pilot study primarily validated the devised fuzzy logic method and the functional prototype of the mobile application, demonstrating its potential to leverage universal smartphone accelerometers for accessible remote monitoring. Using fuzzy logic, temporal and behavioral symmetry in movement patterns were adapted to detect asymmetric anomalies, e.g., abnormal inactivity or falls. The study is particularly relevant considering lonely individuals found deceased in their homes long after dying. By providing real-time monitoring and proactive alerts, this eHealth solution offers a scalable, cost-effective approach to improving elderly care, enhancing safety, and reducing the risk of unnoticed deaths through fuzzy logic. Full article

The current research examines the issue of social inclusivity in the context of digitalization of smart city governance and explores ways to overcome the digital divide, which impedes equal access to online services for vulnerable population groups (elderly people, people with disabilities, low-income individuals, and residents of remote areas). Based on a literature review, the study outlines three generations of the digital divide: access, digital skills, and the ability to derive socio-economic benefits. A methodology is proposed that combines cluster analysis of 27 European cities using 11 integrated indicators, aimed at identifying typical development profiles while accounting for infrastructure, air quality, and levels of digital literacy. The results revealed four clusters: “Digital Leaders with Environmental Awareness”, “Mid-Level Cities with Development Potential”, “Opportunities with Environmental Challenges”, and “Advanced Digital Hubs with High Quality of Life”. The study confirms the hypothesis regarding the effectiveness of a comprehensive approach that integrates infrastructure investment, educational programs, and inclusive planning. The article’s conclusions emphasize the need to apply universal design principles, subsidize internet access, and regularly monitor digital inclusion indices to achieve SDGs 11 and 16. Full article

The rapid growth of China’s aging population has made elderly care a pressing social issue. Due to an imperfect pension system, limited uptake of institutional care, and uneven regional economic development, most elderly people in China still rely on home-based care. Elderly people living at home are usually cared for by their family, partners, caregivers, or themselves. However, this often fails to meet their complex health, safety, and emotional needs. Artificial intelligence may provide promising solutions to improve home care experiences and address the multifaceted health and lifestyle challenges faced by homebound elderly people. This review explores the applications of artificial intelligence in home-based care from four main perspectives: home health care, home safety and security, smart life assistants, and psychological care and emotional support. We systematically searched PubMed, IEEE Xplore, CNKI, and Scopus databases, integrated the latest research published between 2015 and 2024, focused on peer-reviewed, practice-oriented research, and reviewed relevant technology development paths and the current status of the field. Unlike previous studies that focused on physiological monitoring, this study is the first to systematically and comprehensively evaluate the role of artificial intelligence in improving the convenience of daily life and mental health support for elderly people at home. By comprehensively reviewing and analyzing the basic principles and application background of artificial intelligence technology in this field, we summarize the current technical and ethical challenges and propose future research directions. This study aims to help readers gain a deeper understanding of the current status and emerging trends of artificial intelligence-enabled home-based elderly care, thereby providing valuable insights for continued innovation and application in this rapidly developing field. Full article

Background: Influenza poses a significant threat to the health of Malaysians, particularly among the elderly population. It results in high levels of illness and mortality, becoming a financial burden on the government. Vaccination is widely recognised as the most effective measure for controlling the spread and impact of influenza. Objectives: This study sought to assess the knowledge, attitudes, and practices (KAP) regarding influenza and influenza vaccination among older adults attending primary healthcare centres in different states of Malaysia. Additionally, the study assessed the level of acceptance for a proposed free annual influenza vaccination program. Methods: A nationwide survey was conducted involving 672 older people aged 60 and above who visited nine primary healthcare centres in Malaysia. These centres were selected using proportionate to population size (PPS) sampling to ensure representation from each zone. Participants completed a validated self-reported questionnaire. Descriptive statistics were used to determine the levels of KAP, and a binomial logistic regression model was used to determine the predictors of acceptance for the proposed free annual vaccination program. Results: Most participants displayed a strong understanding of influenza illness (74.0%) and the vaccine (65.9%). Moreover, 76.4% of respondents exhibited a positive attitude towards influenza vaccination. However, the prevalence of good vaccination practices was relatively low, with only 29.2% of participants having a history of previous vaccination, and just 55.2% of these consistently practicing annual vaccination. The group acceptance rate for the proposed free annual influenza vaccination was 62.3%. Significant predictors of acceptance included a history of previous vaccination (good practice) (OR = 6.438, 95% CI = 1.16–35.71, p < 0.001), a positive attitude towards vaccines (OR = 21.98, 95% CI = 5.44–88.87, p = 0.033), and a good level of knowledge about the influenza vaccine (OR = 0.149, 95% CI = 0.03–0.79, p = 0.026). Conclusions: Increasing the uptake of influenza vaccination among the older population in Malaysia remains a significant challenge. It is recommended that a targeted, free annual influenza vaccination program be implemented for high-risk populations, particularly those with comorbidities and those who have shown greater receptiveness. In addition, health education strategies aimed at raising awareness and understanding of influenza should be prioritised. Strengthening epidemiological data collection and establishing systematic monitoring mechanisms are also essential to support these efforts. Full article

The aging population has created a significant challenge affecting the world; social and healthcare systems need to ensure elderly individuals receive the necessary care services to improve their quality of life and maintain their independence. In response to this need, developing integrated digital solutions, such as IoT based wearable devices combined with artificial intelligence applications, offers a technological platform for creating Ambient Intelligence (AI) and Assisted Living (AAL) environments. These advancements can help reduce hospital admissions and lower healthcare costs. In this context, this article presents an IoT application based on MEMS (micro electro-mechanical systems) sensors integrated into a state-of-the-art microcontroller (STM55WB) for recognizing the movements of older individuals during daily activities. human activity recognition (HAR) is a field within computational engineering that focuses on automatically classifying human actions through data captured by sensors. This study has multiple objectives: to recognize movements such as grasping, leg flexion, circular arm movements, and walking in order to assess the motor skills of older individuals. The implemented system allows these movements to be detected in real time, and transmitted to a monitoring system server, where healthcare staff can analyze the data. The analysis methods employed include machine learning algorithms to identify movement patterns, statistical analysis to assess the frequency and quality of movements, and data visualization to track changes over time. These approaches enable the accurate assessment of older people’s motor skills, and facilitate the prompt identification of abnormal situations or emergencies. Additionally, a user-friendly technological solution is designed to be acceptable to the elderly, minimizing discomfort and stress associated with using technology. Finally, the goal is to ensure that the system is energy-efficient and cost-effective, promoting sustainable adoption. The results obtained are promising; the model achieved a high level of accuracy in recognizing specific movements, thus contributing to a precise assessment of the motor skills of the elderly. Notably, movement recognition was accomplished using an artificial intelligence model called Random Forest. Full article

Legionella bacterium has the aquatic environment as its natural reservoir. In humans, it can cause a form of interstitial pneumonia called legionellosis which can be transmitted by inhalation of contaminated water aerosols. Legionella infection occurs more frequently in certain more susceptible population groups, including smokers, alcoholics, men, the elderly, as well as people with acquired immunodeficiency syndrome, hematological cancers, and diabetes mellitus. This study aimed to evaluate the effectiveness of the new Italian National Guidelines for the prevention of Legionella colonization in water systems application by analyzing the environmental monitoring data of Legionella carried out in healthcare facilities in the Campania region from 2019 to 2022. The secondary objectives were to estimate the most observed serogroups of L. pneumophila and to analyze the possible link between water temperature and the presence of Legionella, respectively. From our data, it emerged that in 2019, 41.1% of the examined facilities were contaminated by the Legionella genus; in 2020, the contamination percentage was 42.9%; in 2021, it was 54.5%; in 2022, it was 45.5%. Instead, the Legionella positivity rate decreased from 2019 (54.3%) to 2022 (52.4%), suggesting a possible positive influence of more restrictive prevention and control measures. The prevalent species was Legionella pneumophila, particularly serogroup 1; water temperature was the risk factor implicated in Legionella contamination. Full article

As global warming intensifies heatwave events, their impact on human health is becoming increasingly significant. To further understand the dynamic response of humans to heatwaves, this study selected samples from different age groups under various thermal conditions in Beijing, China. Physiological parameters, including blood pressure, blood glucose, heart rate, blood oxygen, and body temperature, were monitored during both heatwave and non-heatwave periods using wearable devices. The results show that during heatwaves, the average blood pressure increased by about 10%, blood glucose levels rose by about 4%, heart rates increased by about 12%, and blood oxygen levels decreased by an average of about 7%. These effects were particularly pronounced in people aged 50 and above, with heart rate and blood oxygen saturation showing significant age-related differences. The study indicates that heat waves have a more substantial impact on the elderly population, with age being a key factor in determining physiological responses to extreme heat. Therefore, it is necessary to develop age-specific health management strategies to address vulnerability under high-temperature conditions. Full article

The ever-evolving landscape of healthcare demands innovative solutions, particularly in light of the global health crisis of 2020 and the aging global population. Technological advancements and new approaches in remote health monitoring systems have helped to bridge the gap for vulnerable individuals such as older adults. This review explores methods for the analysis of physiological signals using remote and intelligent systems and mobile and web-based applications, mostly linked to wearable devices, focusing primarily on the elderly population. The main objective is to identify crucial advancements in the development or integration of technology applied to addressing challenges of this magnitude. The research is structured following the PRISMA-ScR guidelines. The search strategy was implemented in databases such as the ACM Digital Library, IEEE Xplore, PubMed, Science Direct, Scopus, and Springer Link. A total of 411 articles were collected, and inclusion and exclusion criteria were applied to focus on studies published between 2020 and 2024. Ultimately, 100 articles from 35 countries were selected for data extraction. The findings reveal significant progress in remote monitoring technologies but emphasize the need for rigorous validation to ensure accuracy and reliability across diverse populations. To develop robust systems that provide equitable and high-quality healthcare, it is essential to address critical challenges such as data privacy, security, accessibility, and ethical considerations. Full article

The aging global population requires innovative remote monitoring systems to assist doctors and caregivers in assessing the health of elderly patients. Doctors often lack access to continuous behavioral data, making it difficult to detect deviations from normal patterns when elderly patients arrive for a consultation. Without historical insights into common behaviors and potential anomalies detected with unobtrusive techniques (e.g., non-wearable devices), timely and informed medical interventions become challenging. To address this, we propose an edge-based Internet of Things (IoT) framework that enables real-time monitoring and anomaly detection using non-wearable sensors to assist doctors and caregivers in assessing the health of elderly patients. By processing data locally, the system minimizes privacy concerns and ensures immediate data availability, allowing healthcare professionals to detect unusual behavioral patterns early. The system employs advanced machine learning (ML) models to identify deviations that may indicate potential health risks. A prototype of our system has been developed to test its feasibility and demonstrate, through the application of two of the most frequently used ML models, i.e., isolation forest and Long Short-Term Memory (LSTM) networks, that it can provide scalability, efficiency, and reliability in the context of elderly care. Further, the provided dashboard enables caregivers and healthcare professionals to access real-time alerts and longitudinal trends, facilitating proactive interventions. The proposed approach improves healthcare responsiveness by providing instant insights into patient behavior, facilitating more accurate diagnoses and interventions. This study lays the groundwork for future advancements in the field and offers valuable insights for the research community to harness the full potential of combining edge computing, artificial intelligence (AI), and the IoT in elderly care. Full article

This study aims to enhance human pose detection performance through image preprocessing. With the growing global elderly population, supporting in-home elderly individuals has become increasingly crucial, especially in Japan, where approximately 90% of the elderly live at home. In this study, abnormal behaviors in in-home elderly individuals were detected using high-precision pose detection technologies, such as the MoveNet model, based on machine learning. However, there was a discrepancy in pose detection accuracy between the standing and lying positions. Hence, preprocessing techniques such as image rotation, flipping, resizing, and noise reduction were applied, and their effects were analyzed in detail. As a result, an improvement in pose detection accuracy for most body parts, except for specific regions, was observed. By constructing an optimal preprocessing pipeline, it is expected to reduce false detection and missed detection, contributing to the practical application of pose detection technology. Full article

Recently, health risk assessment and early warning systems for high-temperature events have become critical concerns. However, current high-temperature warning systems primarily focus on temperature alone, which fails to accurately reflect the actual heat exposure levels and associated health risks. Therefore, this paper proposes an improved AHP (analytic hierarchy process) combined with a multi-level gray evaluation method for assessing human health risks during high-temperature conditions. A comprehensive early warning system is developed, incorporating various indicators, including human status, building conditions, and weather forecasts, making it more holistic than traditional temperature-based warning systems. A case study shows that the highest evaluation score for young individuals is 3.41, while elderly males receive the highest score of 2.5. Furthermore, the highest evaluation score for males is 3.41, while for females the highest score of 3.1. The warning results indicate that for young individuals, no alert is issued; for the elderly, a red alert is triggered; and for middle-aged individuals, the system issues orange and yellow alerts based on varying levels of risk. This study can be used to monitor health risk and provide alert message to humans. Based on the proposed early warning system, people can be able to predict health risk in time. Full article

Non-Intrusive Load Monitoring (NILM) includes a set of methods orientated to disaggregating the power consumption of a household per appliance. It is commonly based on a single metering point, typically a smart meter at the entry of the electrical grid of the building, where signals of interest, such as voltage or current, can be measured and analyzed in order to disaggregate and identify which appliance is turned on/off at any time. Although this information is key for further applications linked to energy efficiency and management, it may also be applied to social and health contexts. Since the activation of the appliances in a household is related to certain daily activities carried out by the corresponding tenants, NILM techniques are also interesting in the design of remote monitoring systems that can enhance the development of novel feasible healthcare models. Therefore, these techniques may foster the independent living of elderly and/or cognitively impaired people in their own homes, while relatives and caregivers may have access to additional information about a person’s routines. In this context, this work describes an intelligent solution based on deep neural networks, which is able to identify the daily activities carried out in a household, starting from the disaggregated consumption per appliance provided by a commercial smart meter. With the daily activities identified, the usage patterns of the appliances and the corresponding behaviour can be monitored in the long term after a training period. In this way, every new day may be assessed statistically, thus providing a score about how similar this day is to the routines learned during the training interval. The proposal has been experimentally validated by means of two commercially available smart monitors installed in real houses where tenants followed their daily routines, as well as by using the well-known database UK-DALE. Full article