Integrated telehomecare system framework for seamless access to health records

The number of patients suffering from chronic diseases is increasing in every hospital, especially patients at high risk such as the elderly. This causes congestion in the hospital because chronic patients need to perform regular checkups. If the patient is outside the living area and needs an examination, the patient must perform a re-registration process as the patient information is not in contact with each other. Through the telehomecare system, patients can perform health screenings at home through a remote data innovation system. This innovation is observed to be practical and easy to use, while providing better access to health care without compromising on quality. The telehomecare framework may provide screening care to high-potential patients at home by means of CCTV, biometric sensors, vital sign monitors and other electronic devices. However, before telehomecare technology can be implemented, an understanding of the healthcare framework and the adoption of the technology among health workers and patients is important to investigate. The objective of the study is to compare and contrast existing telehomecare models that can be used for the proposed framework through telehomecare, explore existing nursing processes through primary data collection through case study, development of a proposed framework to integrate electronic medical records through telehomecare and validate the proposed framework. The purpose of this research is to analyze and understand how to integrate the telehomecare system framework for seamless access to health records. Key data collection was done through field studies in several health service organizations throughout Malaysia. Data collected from case studies such as health worker awareness and barriers will be used as input to propose the telehomecare system framework to integrate home health records and medical records of health facilities in hospital

A Conceptual Integrated Health Information Systems Framework In Postnatal Care For Modern And Traditional Malay Medicine

This paper proposes an integrated health information systems framework for Traditional Malay Medicine (TMM) and modern medicine in the field of postnatal care. A qualitative study was conducted via healthcare experts in the field of modern medicine and Traditional Malay Medicine to assess the current situation and identify the research gap and point of isolation between both traditional and modern medicine field. A total of 26 healthcare practitioners whom represented five different set of healthcare organisations participated in this study. The healthcare practitioners consist of modern and traditional Malay medicine background with and without proper training. Results show that there is a gap in the current people, process and technology areas of the current framework. A novel conceptual framework, MyPostnatal, proposes the existence of a sufficiently generic, extensible in-formation model where new data sources can be integrated without major changes to the data scheme. Human and organization factors are also highlighted to stimulate the adoption towards electronic health records

YOLOv5 Model-Based Real-Time Recyclable Waste Detection and Classification System

Emerging nations, driven by population growth and rapid urbanization, generate significant waste. Inadequate waste management systems prevail in many countries, including Malaysia, due to a lack of understanding and insufficient infrastructure. Despite poor waste management, there needs to be an automated classification system, leading to time-consuming manual recycling processes. The project aims to develop a real-time waste identification and classification system. The project’s objectives are: 1) design a prototype using a web application and a real-time video platform to detect and categorize recyclable waste; 2) develop the prototype utilizing the YOLOv5 model; and 3) test the model’s accuracy. In the real-time video environment, the system can identify the type of waste and the corresponding recycle bin colors for proper disposal. The model achieved an accuracy rate of 86.25% in identifying and detecting the waste

Utilising the YOLOv3 Algorithm for the Student Posture Recognition System in Classroom Settings

Effective student-teacher interaction helps transfer knowledge, clarify concepts, and create a conductive learning environment. The effectiveness of the interaction can be seen through students’ behaviour and various factors, such as pos- ture and gestures in the classroom. However, educators face significant difficulties in tracking each student’s performance and behaviour during class. Therefore, this study focuses on student posture recognition in classroom settings, which is essential for monitoring student behaviour and engagement during lectures. The proposed system utilises the YOLOv3 machine learning model for real-time detection. A dataset of student postures was collected from Google Images, and the data was used to train a deep neural network. The model was then tested on classroom images and compared to manual annotations. The results showed that the model can accurately recognise student postures with high precision, recall, F1-score, and mean average precision (mAP), achieving an average precision of 88%, recall of 89%, F1-score of 88%, and mAP of 95.20%. The real-time processing capability of YOLOv3 allows for immediate posture detection during lectures in a classroom; this may help educators monitor student behaviour and engagement