Masayuki Ono, Kunihiro Nishimura, T. Tanikawa, M. Hirose
{"title":"Neural network based event estimation on lifelog from various sensors","authors":"Masayuki Ono, Kunihiro Nishimura, T. Tanikawa, M. Hirose","doi":"10.1109/VSMM.2010.5665962","DOIUrl":null,"url":null,"abstract":"The data related to our life experiences is called lifelog, which can easily be collected with mobile electronic devices in recent years. Although lifelog research has been conducted for a long time, practical applications such as a memory assistant system have not been fully developed yet. This is mainly due to the lack of methods to structurize the lifelog data efficiently. In our research, we developed a method for structuring a lifelog consisting of data from various sensors, focusing on event estimation with neural network. In an evaluation experiment, we captured lifelog data with a device that has various sensors, and then we estimated the events, i.e., the participantsf activities. As a result, the system correctly estimated events 70.4% of the time. We also created a lifelog viewer to visualized the data based on the result of event estimation.","PeriodicalId":348792,"journal":{"name":"2010 16th International Conference on Virtual Systems and Multimedia","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 16th International Conference on Virtual Systems and Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VSMM.2010.5665962","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The data related to our life experiences is called lifelog, which can easily be collected with mobile electronic devices in recent years. Although lifelog research has been conducted for a long time, practical applications such as a memory assistant system have not been fully developed yet. This is mainly due to the lack of methods to structurize the lifelog data efficiently. In our research, we developed a method for structuring a lifelog consisting of data from various sensors, focusing on event estimation with neural network. In an evaluation experiment, we captured lifelog data with a device that has various sensors, and then we estimated the events, i.e., the participantsf activities. As a result, the system correctly estimated events 70.4% of the time. We also created a lifelog viewer to visualized the data based on the result of event estimation.