Integrating Artificial Intelligence and Wearable IoT System in Long-Term Care Environments.

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.

An Automated Clubbed Fingers Detection System Based on YOLOv8 and U-Net: A Tool for Early Prediction of Lung and Cardiovascular Diseases.

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.

The secure authorization model for healthcare information system.

Exploring healthcare system for assisting medical services or transmitting patients' personal health information in web application has been widely investigated. Information and communication technologies have been applied to the medical services and healthcare area for a number of years to resolve problems in medical management. In the healthcare system, not all users are allowed to access all the information. Several authorization models for restricting users to access specific information at specific permissions have been proposed. However, as the number of users and the amount of information grows, the difficulties for administrating user authorization will increase. The critical problem limits the widespread usage of the healthcare system. This paper proposes an approach for role-based and extends it to deal with the information for authorizations in the healthcare system. We propose the role-based authorization model which supports authorizations for different kinds of objects, and a new authorization domain. Based on this model, we discuss the issues and requirements of security in the healthcare systems. The security issues for services shared between different healthcare industries will also be discussed.

Secure mobile agent for telemedicine based on P2P networks.

Exploring intelligent mobile agent (MA) technology for assisting medical services or transmitting personal patient-health information in telemedicine applications has been widely investigated. Conversely, peer-to-peer (P2P) networking has become one of the most popular applications used in the Internet because of its benefits for easy-to-manage resources and because it balances workloads. Therefore, constructing an agent-based telemedicine platform based on P2P networking architecture is necessary. The main purpose of this paper is to construct a safe agent-based telemedicine that based on P2P networking architecture. Two themes are addressed in this paper: (a) the P2P network architecture for an agent-based telemedicine service, and (b) the security mechanisms for the proposed telemedicine networking architecture. When an MA contains patient information and migrates from one host to another through the Internet, it can be attacked by other software agents or agent platforms that can illegally access patient information. The proposed P2P network architecture is based on the JXTA protocol and provides two types of telemedicine service models: the predictable service model and unpredictable service model. This architecture employs a two-layer safety mechanism for MAs (i.e., time-limited black boxes and RSA undetachable signature technologies), to provide a secure solution for agent-based telemedicine services.

Dose-Response Relationships of Multisensory Intervention on Hospitalized Patients With Chronic Schizophrenia.

BACKGROUND: Patients with chronic schizophrenia often show negative emotional responses because of cognitive impairment. Multisensory stimulation therapy has been shown effective in improving cognitive and emotional functions in cognitively impaired patients with dementia. However, very few studies have applied this multisensory intervention to patients with chronic schizophrenia. Furthermore, it is not known whether a dose-response relationship exists between the frequency and efficacy of this intervention. PURPOSE: The objective of this study was to evaluate the relationship between the effects and dose-response of the multisensory intervention on patients with chronic schizophrenia in a mental rehabilitation hospital. METHODS: A crossover study design with a sample size of 60 was conducted. Participants were divided equally between an experimental group and a control group. The experimental group received the intervention in a multisensory stimulation room. Six sessions of the 30-minute sensory intervention were conducted on experimental group participants, who were allowed to select their preferred stimulants. In contrast, the control group received routine care only. After a 2-week washout period, participants in the experimental group were reassigned to the control group, and those in the control group were reassigned to the experimental group. Standardized questionnaires were applied to evaluate psychotic symptoms, emotional responses, and well-being of all participants. The NeXus-4 model wireless biofeedback system was used to measure the psychological-physiological parameters of participants at baseline and after the first, third, and sixth sessions of the multisensory intervention. A generalized estimating equation model was used to analyze the effects of the intervention RESULTS:: Although multisensory stimulation therapy had no significant effect on psychotic symptoms and well-being, this intervention may improve the negative emotional reactions of patients. In particular, the intervention significantly reduced the subjective anxiety level of participants and stabilized their objective respiratory and heart rates. Moreover, a positive correlation was found between the efficacy of the treatment and the frequency of the intervention. CONCLUSIONS/IMPLICATIONS FOR PRACTICE: Evidence from this study validated the multisensory stimulation therapy protocol. Therefore, this protocol may be incorporated into clinical interventions to improve quality of care and to alleviate the negative emotions of patients with chronic schizophrenia.