Abstract: Data security in u-Health system can be an important
issue because wireless network is vulnerable to hacking. However, it is
not easy to implement a proper security algorithm in an embedded
u-health monitoring because of hardware constraints such as low
performance, power consumption and limited memory size and etc. To
secure data that contain personal and biosignal information, we
implemented several security algorithms such as Blowfish, data
encryption standard (DES), advanced encryption standard (AES) and
Rivest Cipher 4 (RC4) for our u-Health monitoring system and the
results were successful. Under the same experimental conditions, we
compared these algorithms. RC4 had the fastest execution time.
Memory usage was the most efficient for DES. However, considering
performance and safety capability, however, we concluded that AES
was the most appropriate algorithm for a personal u-Health monitoring
system.
Abstract: In this paper, we present user pattern learning
algorithm based MDSS (Medical Decision support system) under
ubiquitous. Most of researches are focus on hardware system, hospital
management and whole concept of ubiquitous environment even
though it is hard to implement. Our objective of this paper is to design
a MDSS framework. It helps to patient for medical treatment and
prevention of the high risk patient (COPD, heart disease, Diabetes).
This framework consist database, CAD (Computer Aided diagnosis
support system) and CAP (computer aided user vital sign prediction
system). It can be applied to develop user pattern learning algorithm
based MDSS for homecare and silver town service. Especially this
CAD has wise decision making competency. It compares current vital
sign with user-s normal condition pattern data. In addition, the CAP
computes user vital sign prediction using past data of the patient. The
novel approach is using neural network method, wireless vital sign
acquisition devices and personal computer DB system. An intelligent
agent based MDSS will help elder people and high risk patients to
prevent sudden death and disease, the physician to get the online
access to patients- data, the plan of medication service priority (e.g.
emergency case).
Abstract: In ubiqutious healthcare environment, user's health data are transfered to the remote healthcare server by the user's wearable system or mobile phone. These collected user's health data should be managed and analyzed in the healthcare server, so that care giver or user can monitor user's physiological state. In this paper, we designed and developed the intelligent Healthcare Server to manage the user's health data using CDSS and ontology. Our system can analyze user's health data semantically using CDSS and ontology, and report the result of user's physiological raw data to the user and care giver.
Abstract: The objective of this paper is to design a model of human vital sign prediction for decreasing prediction error by using two states mapping based time series neural network BP (back-propagation) model. Normally, lot of industries has been applying the neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has a residual error between real value and prediction output. Therefore, we designed two states of neural network model for compensation of residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We found that most of simulations cases were satisfied by the two states mapping based time series prediction model compared to normal BP. In particular, small sample size of times series were more accurate than the standard MLP model. We expect that this algorithm can be available to sudden death prevention and monitoring AGENT system in a ubiquitous homecare environment.
Abstract: In this study, we introduced a communication system
where human body was used as medium through which data were
transferred. Multiple biosignal sensing units were attached to a subject
and wireless personal area network was formed. Data of the sensing
units were shared among them. We used wideband pulse
communication that was simple, low-power consuming and high data
rated. Each unit functioned as independent communication device or
node. A method of channel search and communication among the
modes was developed. A protocol of carrier sense multiple
access/collision detect was implemented in order to avoid data
collision or interferences. Biosignal sensing units should be located at
different locations due to the nature of biosignal origin. Our research
provided a flexibility of collecting data without using electrical wires.
More non-constrained measurement was accomplished which was
more suitable for u-Health monitoring.