The eLog library was initially developed as a research prototype and later published for lifelogging researchers in 2010 to help them easily analyze heterogenous data and complex visualization. It has been kept growing with the progress of mobile computing environments and recently its UI part is released with GPL v3 license for wider usage. The eLog UI library is optimized for mobile environment and can get easily integrated with existing Web services.

Who We Are

The original work was proposed by Pil Ho and later extended the work with collaboration with 28 researchers around the world who contributed their lifelogs, collaborated for lifelog analysis and share research results to build up an open lifelogging platform for the public. Pil Ho has been keeping the development updating the library following up the progress in mobile computing.


  • Nov. 2014: Change the web page skin using bootstrap.
  • Nov. 2014: Published elog UI library as GPL v3.
  • Oct. 2014: Version up eLog library and documentation.



eLifeLog.org Labs is a collection of on-going experiments by members. Feel free to create your own one to show off your ideas and get some feedbacks or help from experts in eLifeLog.


e-Log Project

The E-log project is an acronym of our main lifelog research works composed of various sub laboratories (See the below) aiming to develop a system for life logging that may cover personal diary, healthcare, fitness, surveillance, security and/or enterprise management: e-Log Project

Lifelog Mining

Lifelog data is diverse in all aspects. This category is a collection of lifelog data analysis using our own data, contributed data or public data. Labs include preliminary data analysis or technical development for lifelog mining: Lifelog Mining

Sensor Development

Lifelogging starts from objective monitoring of a user digitizing human actions using various sensors. This category includes our hands-on experiments testing or developing various lifelogging sensors: Sensor

Student projects

Implementing Event summarization on LifeLog Data

We will try to build a system that is capable to summarize the images taken in near similar location along with GPS data. Image grouping will be done using different clustering techniques. Image clustering is a process of grouping images based on their similarity. The image clustering usually uses the color component, texture, edge, shape, or mixture of two components, etc. The clustered image will help in exact event summarization in the particular location.

Summarization of lifelog data

We want to address the problem of summarising frequent event that happens during a user–specified period of time. The key idea that drives our solution is to proceed with the summarisation by steps, as it follows: (1) GPS Points: first of all we need to extract all the paths that the subject has done in the given period of time. With this data we can recognize the most frequent path; (2) People: from the paths it is possible to extract the people that the subject have encounter more times, in order to get the frequent person seen; and (3) Images: with the new information we can now get the most interesting pictures that can describe the place and the people seen in this period of time. The photos will be selected from the ones captured on the starting and arrival place and we use some criteria to choose which one is better.
* For students, to have your project listed in this page, please create a child book page with your experiment name of this Labs. Describe your experiment and plans. Please actively update your project status to keep us informed on what works or not. It would be the best way to get advised from many research members in this community :)

List of all labs