Wireless health data exchange for home healthcare monitoring systems.

Ubiquitous home healthcare systems have been playing an increasingly significant role in the treatment and management of chronic diseases, such as diabetes and hypertension, but progress has been hampered by the lack of standardization in the exchange of medical health care information. In an effort to establish standardization, this paper proposes a home healthcare monitoring system data exchange scheme between the HL7 standard and the IEEE1451 standard. IEEE1451 is a standard for special sensor networks, such as industrial control and smart homes, and defines a suite of interfaces that communicate among heterogeneous networks. HL7 is the standard for medical information exchange among medical organizations and medical personnel. While it provides a flexible data exchange in health care domains, it does not provide for data exchange with sensors. Thus, it is necessary to develop a data exchange schema to convert data between the HL7 and the IEEE1451 standard. This paper proposes a schema that can exchange data between HL7 devices and the monitoring device, and conforms to the IEEE 1451 standard. The experimental results and conclusions of this approach are presented and show the feasibility of the proposed exchange schema.

A monitoring and advisory system for diabetes patient management using a rule-based method and KNN.

Diabetes is difficult to control and it is important to manage the diabetic's blood sugar level and prevent the associated complications by appropriate diabetic treatment. This paper proposes a system that can provide appropriate management for diabetes patients, according to their blood sugar level. The system is designed to send the information about the blood sugar levels, blood pressure, food consumption, exercise, etc., of diabetes patients, and manage the treatment by recommending and monitoring food consumption, physical activity, insulin dosage, etc., so that the patient can better manage their condition. The system is based on rules and the K Nearest Neighbor (KNN) classifier algorithm, to obtain the optimum treatment recommendation. Also, a monitoring system for diabetes patients is implemented using Web Services and Personal Digital Assistant (PDA) programming.

An evolution based biosensor receptor DNA sequence generation algorithm.

A biosensor is composed of a bioreceptor, an associated recognition molecule, and a signal transducer that can selectively detect target substances for analysis. DNA based biosensors utilize receptor molecules that allow hybridization with the target analyte. However, most DNA biosensor research uses oligonucleotides as the target analytes and does not address the potential problems of real samples. The identification of recognition molecules suitable for real target analyte samples is an important step towards further development of DNA biosensors. This study examines the characteristics of DNA used as bioreceptors and proposes a hybrid evolution-based DNA sequence generating algorithm, based on DNA computing, to identify suitable DNA bioreceptor recognition molecules for stable hybridization with real target substances. The Traveling Salesman Problem (TSP) approach is applied in the proposed algorithm to evaluate the safety and fitness of the generated DNA sequences. This approach improves efficiency and stability for enhanced and variable-length DNA sequence generation and allows extension to generation of variable-length DNA sequences with diverse receptor recognition requirements.