Abstract:
PURPOSE: A system and a method for providing health care using a universal health platform are provided to collect health data of PHD standard health care device through measurement platform in living space of individual and transfer the health data to information collecting platform, thereby collectively managing health information of the individual. CONSTITUTION: A measuring platform(200) collects personal health data of a PHD(Personal Health Device) standard from at least one or more personal health devices(110-160). The measurement platform changes the individual health data into CDA(Clinical Document Architecture) standard data. The measurement platform transmits the CDA standard data to information collecting platform(300). The information collecting platform generates complex CDA standard data through the CDA standard data. The information collecting platform provides the complex CDA standard data to services(410-430).
Abstract:
PURPOSE: A regular day behavior recognition system for analyzing behaviors included in a daily life by using various sensors is provided to rapidly deal with various situations by transmitting related information to a management center according to a regular behavior. CONSTITUTION: A sensor integration receiving module(110) recognizes a performer from sensors. The sensor integration receiving module receives the signals about a battery of the sensors. A go-out recognizer analyzes 'the time when doer is acknowledged' and battery. The going out whether or not of doer is recognized. A database(130) stores a doer recognition time.
Abstract:
Apparatus and method for refining subject activity classification for the recognition of daily activities of a subject, and a system for recognizing daily activities using the same. The refining apparatus improves the correctness of subject activity classification using daily activities of a subject, activation time information of sensors mounted on objects associated with the daily activities of the subject, and the suitability of a continuous activity pattern in relation to the daily activities. This improves the correctness of subject activity classification that becomes basic information in daily activity analysis.
Abstract:
An apparatus and method for refining subject activity classification to recognize an activity of a daily life, and a system for recognizing activity of daily life using the same are provided to increase an accuracy of a behavior classification of a subject person by amending improper behavior among a instantaneous behavior and continuous behavior pattern of the classified subject person. An input buffer receives and stores a behavior classification value of a classified subject person(S200). A first calibrating module extracts and reads a behavior classification value of the subject person stored in the input buffer as the specified interval(S210). The extracted specified interval of a behavior classification value of the subject person is determined whether instantaneous behavior data is in the interval or not(S220). A calibration target data which is an instantaneously behavior data is amended(S230). If no instantaneous behavior data is found, the appropriateness of a continuous action pattern of a behavior classification value of the specified interval of the inside subject person is determined(S240). If an inappropriate pattern is found, an active time band of a object response sensor related to a behavior data of the subject person is considered, and the behavior classification value of the subject person corresponding to the inappropriate pattern is amended(S250). An amended behavior classification value is outputted(S260).
Abstract:
A clustering method of gene expressed profile by using gene ontology(GO) is provided to improve biological meaningfulness and reliability of cluster by using the gene ontology as a cluster seed, and minimize loss of information in employment of gene ontology as a cluster seed. A clustering method of gene expressed profile by using gene ontology comprises the steps of: selecting at least one GO term in the gene ontology tree; inputting a gene expression data set; classifying the gene expression data set according to the GO term into each group; firstly clustering the gene expression data belonging to each of the classified groups based on similarity of the gene expression data; and secondly clustering the gene expression data set by using the first clustering results as seed.
Abstract:
An apparatus and a method for predicting a gene module are provided to predict the gene module(a group of genes) controlled by the same transcription factor group and performing the same function in a cell from gene expression data and transcription factor binding information. An apparatus for predicting a gene module(103) comprises: a gene expression similarity matrix generation means(104) which generates a gene expression similarity matrix using gene expression date from the outside gene expression data; a gene expression similarity graph generation means(105) which generates a gene expression similarity graph using the gene expression similarity matrix generated from the gene expression similarity matrix generation means; a crowd portion graph generation means(106) which applies a crowd portion graph algorithm to the gene expression similarity graph to generate a crowd portion graph; and a significant gene module generation means(107) which extracts the transcription factor binding information from the outside transcription factor binding database using the crowd portion graph to generate a significant gene module, wherein the crowd portion graph generation means generates the crowd portion graph accepting the replication of the gene.
Abstract:
A non-restrictive automatic weight measuring apparatus and method are provided to automatically measure the weight of a user and manage a variation in the weight of the user by using a weight management module. A non-restrictive automatic weight measuring apparatus includes at least one load cell(100), a weight measurement module(200) for reading a measured value from the load cell and measuring a weight value while a user does not recognize the measurement, and a weight decision module(300) for receiving the measured weight value, confirming whether the time when the weight value is measured corresponds to a predetermined weight measuring time and whether the weight value satisfies a minimum weight and obtains at least one median satisfying the confirmation to decide a new weight of the user.