Search Results (84)

Virtual reality (VR) has shown promising potential for upper-extremity rehabilitation; however, its successful integration into clinical practice depends not only on therapeutic effectiveness but also on the acceptance of the technology by patients and healthcare professionals alike. Despite growing international research in this area, there is limited evidence on clinical attitudes toward VR rehabilitation in Thailand and other middle-income settings. This study investigates Thai patients’ and clinicians’ perceptions of VR for upper-extremity rehabilitation through two complementary studies focusing on perceived usability and usefulness. The first study evaluated the perceived usability of a VR rehabilitation game using the System Usability Scale (SUS) among 40 first-time VR users divided into younger and senior groups. The younger group reported a higher average SUS score (64.6) than the senior group (55.4). While both groups generally perceived VR rehabilitation positively, senior participants expressed greater concern regarding system complexity, consistency, and the need for technical assistance. Nevertheless, the findings indicate that VR remained an acceptable rehabilitation approach even among elderly first-time users in a population with relatively lower technological readiness. The second study explored clinicians’ perceptions of utilizing VR-generated movement data to support rehabilitation decision-making. Five rehabilitation professionals evaluated the potential usefulness of VR data visualizations for diagnosis and treatment monitoring. Clinicians generally perceived VR data as valuable, particularly for tracking rehabilitation progress rather than diagnostic decision-making. Feedback from interviews also highlighted practical considerations for future implementation, including the importance of normative data, simplified visualization formats, and the feasibility of clinical workflows. By combining patient usability perspectives with clinicians’ evaluations of clinical usefulness, this research provides a broader understanding of the factors influencing VR adoption for upper-extremity rehabilitation in Thailand. The findings contribute contextual evidence from an underrepresented healthcare environment and offer insights relevant to the future deployment of VR-assisted rehabilitation systems in similar socio-economic settings. Full article

The rapid development of Internet of Things technologies has accelerated the deployment of smart home systems. However, perception solutions based on visual sensors remain constrained by illumination sensitivity, occlusion, and privacy concerns. Frequency-modulated continuous-wave (FMCW) millimeter-wave radar provides a promising alternative because it operates independently of lighting conditions, is robust to environmental changes, and preserves user privacy. To address multiple-extended-target tracking in cluttered indoor environments, this paper proposes a high-accuracy tracking algorithm that combines an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, an optimized Nearest-Neighbor Data Association (NNDA) scheme, and an Extended Kalman Filter (EKF). The improved DBSCAN algorithm introduces spatial-extent constraints, velocity-consistency checks, and candidate-cluster validation to cluster raw radar point clouds and convert extended targets into representative point targets with little additional computational cost. The optimized NNDA scheme then integrates clustering information into the association process, improving the matching accuracy between existing tracks and current measurements. Finally, the EKF estimates the state of each target from the associated measurements. Real-world experiments show that the proposed algorithm achieves tracking errors below 0.4 m in typical motion scenarios, maintains continuous tracking in two-person crossing scenarios, and reaches 93.3% counting accuracy in five-person scenarios. These results outperform the tracking system based on the commercial Texas Instruments (TI) IWR6843ISK millimeter-wave radar evaluation board. The proposed method offers a reliable and privacy-preserving sensing solution for smart homes, elderly care, and intelligent building applications. Full article

The demographic shift towards an aging population necessitates innovative solutions for care and mobility support. While commercial quadruped robots like the Unitree Go1 offer dynamic stability, their native following modes often lack the safety margins and predictability required, and they do not consistently follow the user, at times deviating and navigating independently. This paper presents a robust, vision-based, person-following algorithm designed to address these limitations. Utilizing a ZED 2 stereo camera and Robot Operating System (ROS), the system employs a finite state machine to ensure deterministic target tracking. A velocity control strategy partitions the robot’s motion into distinct stability, proportional, and braking zones based on depth data to ensure fluid interaction. The framework was validated on a Unitree Go1 quadruped platform in an outdoor environment involving 90-degree turns to evaluate tracking robustness. By operating in a headless mode, the system achieved a mean processing latency of $66.5\pm 4.3$ ms. Experimental results demonstrated consistent operational stability, 0.0% intrusion into the intimate safety zone, and effective velocity synchronization between $0.47$ and $0.54$ m/s. While this study establishes a robust technical baseline using healthy subjects, it serves as a preliminary development platform; further iterative testing with elderly users in clinical settings is required to move toward deployment. Beyond the evaluated trials, the framework maintained reliable functional performance across various care facility workshops, successfully following the target in all deployment scenarios. These findings establish a stable technical foundation for the future development of robotic walking partners. Full article

We developed a Bluetooth Low Energy-based indoor asset-tracking system oriented toward elderly care and cultural heritage stewardship. The system stabilizes the noisy received signal strength indicator using a Kalman filter, adapts a logarithmic path loss model to local attenuation via dynamic calibration, and estimates positions with an inverse distance weighted centroid. Built on inexpensive beacons and commodity gateways, it supports real-time updates and map-based visualization while remaining easy to deploy and scale across rooms and facilities. We validate the pipeline in a laboratory grid and discuss applicability to workflows such as geofenced reminders, caregiver situational awareness, and collection movement oversight, offering an affordable, interoperable path to reliable indoor tracking for care institutions, museums, and smart buildings. Full article

The global elderly population continues to increase, and the demand for leisure programs that support active aging is growing. In particular, group-based outdoor activities for seniors are often conducted in large public areas such as parks, ecological gardens, and cultural sites. As many of these spaces are now being integrated into smart city infrastructures equipped with IoT-based sensing and location-aware services, opportunities for data-driven safety support are expanding. However, in these wide and crowded environments, a small number of social workers are responsible for supervising many elderly participants, which creates monitoring blind spots. In addition, age-related cognitive and physical decline increases the risk of wandering and sudden health deterioration, making timely detection and response difficult. To address this problem, we propose a safety monitoring system for seniors. The system is based on a cloud platform that collects location data from GPS modules and motion information from embedded sensors on mobile devices. It provides real-time tracking of each participant and periodically evaluates their safety state. When abnormal conditions are detected, alerts are delivered to both social workers and the corresponding senior. A prototype implementation, consisting of a cloud server and mobile applications for social workers and elderly users, has been developed. The system is evaluated through a field test conducted on a university campus. Full article

This paper deals with a privacy-preserving human tracking system that uses multi-property infrared sensor arrays. In the growing field of intelligent elderly care, there is a critical need for monitoring systems that ensure safety without compromising personal privacy. While traditional camera-based systems offer detailed activity recognition, privacy-related concerns often limit their practical application and user acceptance. Consequently, approaches that protect privacy at the sensor level have gained increasing attention. The privacy-preserving human tracking system proposed in this paper protects privacy at the sensor level by fusing data from an ultra-low-resolution $8\times 8$ (64-pixel) passive thermal infrared (IR) sensor array and a similarly low-resolution $8\times 8$ active Time-of-Flight (ToF) sensor. The thermal sensor identifies human presence based on heat signature, while the ToF sensor provides a depth map of the environment. By integrating these complementary modalities through a convolutional neural network (CNN) enhanced with a cross-attention mechanism, our system achieves real-time three-dimensional human tracking. Compared to previous methods using ultra-low-resolution IR sensors, which mostly only obtained two-dimensional coordinates, the acquisition of the Z coordinate enables the system to analyze changes in a person’s vertical position. This allows for the detection and differentiation of critical events such as falls, sitting, and lying down, which are ambiguous to 2D systems. With a demonstrated mean absolute error (MAE) of 0.172 m in indoor tracking, our system provides the data required for privacy-preserving Ambient Assisted Living (AAL) applications. Full article

Safety and security are major priorities in modern society. Especially for vulnerable groups of individuals, such as the elderly and patients with disabilities, providing a safe environment and adequate alerting for debilitating events and situations can be critical. Wearable devices can be effective but require frequent maintenance and can be obstructive or stigmatizing. Video monitoring by trained operators solves those issues but requires human resources, time and attention and may present certain privacy issues. We propose optical flow-based automated approaches for a multitude of situation awareness and event alerting challenges. The core of our method is an algorithm providing the reconstruction of global movement parameters from video sequences. This way, the computationally most intensive task is performed once and the output is dispatched to a variety of modules dedicated to detecting adverse events such as convulsive seizures, falls, apnea and signs of possible post-seizure arrests. The software modules can operate separately or in parallel as required. Our results show that the optical flow-based detectors provide robust performance and are suitable for real-time alerting systems. In addition, the optical flow reconstruction is applicable to real-time tracking and stabilizing video sequences. The proposed system is already functional and undergoes field trials for cases of epileptic patients. Full article

Respiratory syncytial virus (RSV) is a leading cause of respiratory infections in young children, elderly people, and patients with underlying diseases. Solid data on its epidemiology and burden of disease are essential for the implementation of preventive strategies. This review provides for the first time a comprehensive overview on publicly available RSV surveillance resources in Germany. Methods: Public RSV surveillance systems in Germany were identified and, where possible, exemplary data was extracted to provide an overview of the scope of available data, their strengths and limitations. Results: German RSV surveillance systems provide data on both outpatient and inpatient incidence rates, age distribution, and seasonality. Germany’s public health institution, the Robert Koch Institute (RKI), documents RSV cases nationwide based on mandatory reporting. Further, sentinel surveillance by RKI captures outpatient RSV infections as well as severe hospitalized cases. Nationwide, data on inpatients is collected and reported by hospital discharge diagnostic codes. Additional surveillance systems (e.g., clinical-virology.net) provide data on RSV positivity rates stratified by age and gender. Regional surveillance efforts by ten German states provide data on the infection dynamics. Pediatric documentation of age distribution and severity of respiratory diseases via surveillance was initiated by the German Society for Pediatric Infectious Diseases. Reviewing all available sources and data underlines the high clinical burden, especially in infants and older adults during the winter season. Conclusions: Germany’s RSV surveillance systems on the national and regional level support the tracking of incidence rates and seasonal patterns. Notably, pediatric data collection is more thorough, yielding a more comprehensive dataset than that available for adults. Contextualizing reported incidence rates in light of prospective or modeling studies suggests that the official documentation of RSV cases—particularly among adults—is underestimated. 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

(1) Background: A severe decline in knee joint function significantly affects the mobility of the elderly, making it a key concern in the field of geriatric health. To alleviate the pressure on the knee joints of the elderly during daily movements such as sitting and standing, effective biomechanical solutions are required. (2) Methods: In this study, a biomechanical framework was established based on mechanical analysis to derive the transfer relationship between the ground reaction force and the knee joint moment. Experiments were designed to collect knee joint data on the elderly during the sit-to-stand process. Meanwhile, magnetic resonance imaging (MRI) images were processed through a medical imaging control system to construct a detailed digital 3D knee joint model. A finite element analysis was used to verify the model to ensure the accuracy of its structure and mechanical properties. An improved radial basis function was used to fit the pressure during the entire sit-to-stand conversion process to reduce the computational workload, with an error of less than 5%. In addition, a small-target human key point recognition network was developed to analyze the image sequences captured by the camera. The knee joint angle and the knee joint pressure distribution during the sit-to-stand conversion process were mapped to a three-dimensional interactive platform to form a digital twin system. (3) Results: The system can effectively capture the biomechanical behavior of the knee joint during movement and shows high accuracy in joint angle tracking and structure simulation. (4) Conclusions: This study provides an accurate and comprehensive method for analyzing the biomechanical characteristics of the knee joint during the movement of the elderly, laying a solid foundation for clinical rehabilitation research and the design of assistive devices in the field of rehabilitation medicine. Full article

Dexterity of the fingers is crucial in physical function, as it directly impacts daily activities and is closely connected to cognitive function. The production of brain-derived neurotrophic factor (BDNF) is related to the fingertips in motion. In previous research, we developed a finger motion measurement system for the elderly by integrating image recognition technology with a touch panel. However, despite the system’s ability to capture fine-grained coordinate changes at the moment when fingers touch the panel, the experiment was unengaging for participants. Therefore, we improved the system for measuring finger motion to be less exhausting and more enjoyable. We incorporated music and gamification elements at the moments of finger touch. We obtained a selection of representative rhythm tracks and implemented animated materials in gamification. The participants’ fatigue and enjoyment were measured based on “responsiveness” and “focus” using a quantitative evaluation method. Full article

Urban microclimates, which include phenomena such as urban heat islands (UHIs) as well as cooler environments created by shaded areas and green spaces, significantly affect social behavior and contribute to varying levels of social isolation in cities. UHIs, driven by heat-absorbing materials like concrete and asphalt, can increase urban temperatures by up to 12 °C, discouraging outdoor activities, especially among vulnerable populations like the elderly and those with chronic health conditions. In contrast, shaded areas and green spaces, where temperatures can be 2–5 °C cooler, encourage outdoor engagement and foster social interaction. This narrative review aims to synthesize current literature on the relationship between urban microclimates and social isolation, focusing on how UHIs and shaded areas influence social engagement. A comprehensive literature review was conducted, selecting sources based on their relevance to the effects of localized climate variations on social behavior, access to green spaces, and the impact of urban design interventions. A total of 142 articles were initially identified, with 103 included in the final review after applying inclusion/exclusion criteria. Key studies from diverse geographical and cultural contexts were analyzed to understand the interplay between environmental conditions and social cohesion. The review found that UHIs exacerbate social isolation by reducing outdoor activities, particularly for vulnerable groups such as the elderly and individuals with chronic health issues. In contrast, shaded areas and green spaces significantly mitigate isolation, with evidence showing that in specific study locations such as urban parks in Copenhagen and Melbourne, such areas increase outdoor social interactions by up to 25%, reduce stress, and enhance community cohesion. Urban planners and policymakers should prioritize integrating shaded areas and green spaces in city designs to mitigate the negative effects of UHIs. These interventions are critical for promoting social resilience, reducing isolation, and fostering connected, climate-adaptive communities. Future research should focus on longitudinal studies and the application of smart technologies such as IoT sensors and urban monitoring systems to track the social benefits of microclimate interventions. Full article

Artificial intelligence (AI) and assistive robotics can transform older-person care by offering new, personalised solutions for an ageing population. This paper outlines recent advances in AI-driven applications and robotic assistance in silver care, emphasising their role in improved healthcare services, quality of life and ageing-in-place and alleviating pressure on healthcare systems. Advances in machine learning, natural language processing and computer vision have enabled more accurate early diagnosis, targeted treatment plans and robust remote monitoring for elderly patients. These innovations support continuous health tracking and timely interventions to improve patient outcomes and extend home-based care. In addition, AI-powered assistive robots with advanced motion control and adaptive response mechanisms are studied to support physical and cognitive health. Among these, companion robots, often enhanced with emotional AI, have shown potential in reducing loneliness and increasing connectedness. The combined goal of these technologies is to offer holistic patient-centred care, which preserves the autonomy and dignity of our seniors. This paper also touches on the technical and ethical challenges of integrating AI/robotics into eldercare, like privacy and accessibility, and alludes to future directions on optimising AI-human interaction, expanding preventive healthcare applications and creating an effective, ethical framework for eldercare in the digital age. 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

Background: Mild Cognitive Impairment (MCI) is a critical transitional phase between normal aging and dementia, and early detection is essential to mitigate cognitive decline. Traditional cognitive assessment tools, such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), exhibit limitations in feasibility, which potentially and partially affects results for early-stage MCI detection. This study developed and tested a supportive cognitive assessment system for MCI auxiliary identification, leveraging eye-tracking features and convolutional neural network (CNN) analysis. Methods: The system employed eye-tracking technology in conjunction with machine learning to build a multimodal auxiliary identification model. Four eye movement tasks and two cognitive tests were administered to 128 participants (40 MCI patients, 57 elderly controls, 31 young adults as reference). We extracted 31 eye movement and 8 behavioral features to assess their contributions to classification accuracy using CNN analysis. Eye movement features only, behavioral features only, and combined features models were developed and tested respectively, to find out the most effective approach for MCI auxiliary identification. Results: Overall, the combined features model achieved a higher discrimination accuracy than models with single feature sets alone. Specifically, the model’s ability to differentiate MCI from healthy individuals, including young adults, reached an average accuracy of 74.62%. For distinguishing MCI from elderly controls, the model’s accuracy averaged 66.50%. Conclusions: Results show that a multimodal model significantly outperforms single-feature models in identifying MCI, highlighting the potential of eye-tracking for early detection. These findings suggest that integrating multimodal data can enhance the effectiveness of MCI auxiliary identification, providing a novel potential pathway for community-based early detection efforts. Full article