With the rapid advancement of information and communication technology (ICT), big data, and artificial intelligence (AI), intelligent healthcare systems have emerged, including the integration of healthcare systems with capital, the introduction of healthcare systems into long-term care institutions, and the integration of measurement data for care or exposure. These systems provide comprehensive communication and home exposure reports and enable the involvement of rehabilitation specialists and other experts. Silver technology enables the realization of health management in long-term care services, workplace care, and health applications, facilitating disease prevention and control, improving disease management, reducing home isolation, alleviating family burden in terms of nursing, and promoting health and disease control. Research and development efforts in forward-looking cross-domain precision health technology, system construction, testing, and integration are carried out. This integrated project consists of two main components. The Integrated Intelligent Long-Term Care Service Management System focuses on building a personalized care service system for the elderly, encompassing health, nutrition, diet, and health education aspects. The Wearable Internet of Things Care System primarily supports the development of portable physiological signal detection devices and electronic fences.

Background/Objectives: Lung and cardiovascular diseases are leading causes of mortality worldwide, yet early detection remains challenging due to the subtle symptoms. Digital clubbing, characterized by the bulbous enlargement of the fingertips, serves as an early indicator of these diseases. This study aims to develop an automated system for detecting digital clubbing using deep-learning models for real-time monitoring and early intervention. Methods: The proposed system utilizes the YOLOv8 model for object detection and U-Net for image segmentation, integrated with the ESP32-CAM development board to capture and analyze finger images. The severity of digital clubbing is determined using a custom algorithm based on the Lovibond angle theory, categorizing the condition into normal, mild, moderate, and severe. The system was evaluated using 1768 images and achieved cloud-based and real-time processing capabilities. Results: The system demonstrated high accuracy (98.34%) in real-time detection with precision (98.22%), sensitivity (99.48%), and specificity (98.22%). Cloud-based processing achieved slightly lower but robust results, with an accuracy of 96.38%. The average processing time was 0.15 s per image, showcasing its real-time potential. Conclusions: This automated system provides a scalable and cost-effective solution for the early detection of digital clubbing, enabling timely intervention for lung and cardiovascular diseases. Its high accuracy and real-time capabilities make it suitable for both clinical and home-based health monitoring.

This study investigates how gamified and blended instructional design affects student engagement and learning outcomes in an undergraduate cybersecurity course. The course was implemented across two academic years using consistent content and delivery, providing a natural context for cohort-based comparison. Participants included third-year (n = 47) and second-year (n = 52) students enrolled in the same course, enabling analysis of academic-level differences in cognitive, behavioral, and motivational outcomes. A mixed-methods approach was used, combining pre/post-test results, learning management systems (LMS)-based behavioral data (e.g., login frequency, task completion, forum activity), and a student perception survey. Both cohorts showed significant learning gains, but behavioral engagement differed. Second-year students exhibited higher LMS activity and stronger motivational responses to gamification, while third-year students completed tasks more efficiently. Forum participation was positively correlated with learning gains, suggesting that interactive engagement is a key factor in performance. The study provides practical insights into how gamified blended learning, supported by digital learning analytics, can be scaled across diverse student profiles. It contributes to emerging technologies in higher education by demonstrating the role of behavioral data in refining instructional design.

Introduction: Dysarthria, a motor speech disorder caused by neurological damage, significantly hampers speech intelligibility, creating communication barriers for affected individuals. Voice conversion (VC) systems have been developed to address this, yet accurately predicting phonemes in dysarthric speech remains a challenge due to its variability. This study proposes a novel approach that integrates Fuzzy Expectation Maximization (FEM) with diffusion models for enhanced phoneme prediction, aiming to improve the quality of dysarthric voice conversion. Methods: The proposed method combines FEM clustering with Diffusion Probabilistic Models (DPM). Diffusion models simulate noise addition and removal to enhance the robustness of speech signals, while FEM iteratively optimizes phoneme boundaries, reducing uncertainty. The system was trained using the Saarland University Voice Disorder dataset, consisting of dysarthric and normal speech samples, with the conversion process represented in the Mel-spectrogram domain. The framework employs both subjective (Mean Opinion Score, MOS) and objective (Word Error Rate, WER) metrics for evaluation, complemented by ablation studies. Results: Experimental results showed that the proposed method significantly improved phoneme prediction accuracy and overall voice conversion quality. It achieved higher MOSs for naturalness, intelligibility, and speaker similarity compared to existing models like StarGAN-VC and CycleGAN-VC. Additionally, the proposed method demonstrated a lower WER for both mild and severe dysarthria cases, indicating better performance in producing intelligible speech. Discussion: The integration of FEM with diffusion models offers substantial improvements in handling the irregularities of dysarthric speech. The method’s robustness, as evidenced by the ablation studies, shows that it can maintain speech naturalness and intelligibility even without a speaker-encoder. These findings suggest that the proposed approach can contribute to the development of more reliable assistive communication technologies for individuals with dysarthria, providing a promising foundation for future advancements in personalized speech therapy.

This study aims to develop an AI-based intelligent assessment system for assistive devices and accessibility services within the context of long-term care. Utilizing supervised learning algorithms from machine learning, the system analyzes care plan content recorded by case managers and care managers to provide decision-making assistance for the selection of appropriate assistive devices and accessibility services. The system optimizes the existing one-way flow process by proactively suggesting necessary assistive support items based on care plan analysis. Artificial intelligence technology is employed to analyze the contents of care plans recorded by case managers and photo specialists, enabling the system to provide auxiliary decision-making capabilities for assistive aids and barrier-free services. Through machine learning, the activities of daily living (ADLs) and instrumental activities of daily living (IADLs) in the care management evaluation scale are trained, generating a dataset for predicting the Long-Term Care Case-Mix System (CMS). This predictive capability can be utilized by medical staff, individual managers in unit A, or discharge preparation managers for evaluation and planning purposes. The system's integration of AI technology assists care managers in providing more efficient and personalized care services for their clients, simultaneously reducing their burden and enhancing the overall quality of long-term care services. This research contributes to the field of long-term care by introducing an AI intelligent assessment system that improves decision-making in selecting assistive devices and accessibility services. Leveraging machine learning algorithms and analyzing care plan content, the system enhances the efficiency and personalization of care services, benefiting both care managers and clients.