LoggerMan is a tool that helps lifeloggers and researchers to capture many aspects of their computer usage, export them and get insights.Find Out More
LoggerMan will ensure that a screenshot of your current active window is always taken and stored for you. You have the choice to select one of the shooting intervals (every 5, 10, 30 sec..) or the Smart Shooting option. Smart Shooting takes a screenshot:
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app name, window’s title, image name, unix_timestamp_in_miliseconds
Every word you type (except in password fields) is stored locally for you by LoggerMan.
Loggerman can trace all your mouse’s actions.
This module is responsible for tracking copy-paste operations. Any text you copy to the clipboard is captured and logged for you.
This module is designed to track apps transitions. It only logs the current app (regardless of the window). It fires an event every time the current active app changes. This module is an alternative to the Screenshots module in sense of tracking the current app. However, Screenshots module gives more detailed info about the current active window and its title associated with each screenshot.
In addition to the previous Keyboard module that captures words you type, LoggerMan gives you the option to capture timestamps of pressing keyboards’ buttons as well. This is particularly useful for researchers interested in Keystrokes Dynamics.
The current version of LoggerMan comes with a local reporting tool. Currently the reporting tool shows statistics from data captured by only Keyboard,ScreenShot & Mouse modules (so make sure to enable these modules to get the best of it). Other modules capture the data and store them in CSV files ready to be prased and used later on. We are improving our reporting tool to cover the other modules.
This project is part of our lifelogging research in Insight Centre, Dublin City University. We love to hear your feedback and/or your participation in our data gathering task.
This project has been funded by Science Foundation Ireland (SFI): SFI/12/RC/2289
Zaher Hinbarji, Rami Albatal and Cathal Gurrin
In our research, we are trying to build many useful and cool applications based on Human-Computer interaction data. We apply machine learning and data mining technologies on raw data in order to extract meaningful information to the end user. Your data donation helps us to better understand this area. You can get more details by following this linkPotential Applications