Paper Info
Title
An evaluation of input data quality of lifelog analysis application with a framework based on quantitative index
Abstract
In recent years, by the improvement of the data acquisition technology and the development of storage, it has become greatly easier than before to collect lifelog that is to record the person's behavior as digital data. As a result, various lifelog analysis applications have been developed that offer the user profitable information such as person's action histories with an analysis of collected data by sensor terminals, video cameras, and so on. However, in these lifelog analysis applications, the quality of the data that was collected from the sensor terminals and inputted to the application was not discussed in detail. Therefore, in this paper, we have focused on the quality of video image data and the acceleration data of objects. As a representative lifelog analysis application, we have chosen an application which verbalizes person's behavior from the data, and shown the influence of the quality of input data on the execution result of the application by a quantitative index. An evaluation framework is proposed for the discussion of a correlation between input data and execution results of the application. As data processing methods, Bayesian Classifier and HMM are employed in his paper. With various conditions, it has been clarified how the quality of input data affects the result of the lifelog analysis application.
Year
DOI
Venue
2012
10.1145/2184751.2184861
ICUIMC
Keywords
Field
DocType
various lifelog analysis application,execution result,input data,lifelog analysis application,input data quality,sensor terminal,representative lifelog analysis application,quantitative index,data acquisition technology,video image data,digital data,acceleration data,data acquisition,data quality,data processing,bayesian classifier,profitability,hidden markov model,indexation
Data mining,Data processing,Lifelog,Data quality,Naive Bayes classifier,Computer science,Data acquisition,Hidden Markov model,Digital data
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
Akika Yamashita100.34
Saeko Iwaki271.40
Masato Oguchi313853.94