Building Activity Definitions to Recognize Complex Activities Using an Online Activity Toolkit
One of the biggest challenges in the field of activity recognition is gathering training data for building activity inference models. To address this problem, we have developed an online activity toolkit for gathering activity data from online users. We use this data to build activity definitions for use in our system which is based on Context-Driven Activity Theory. We use Markov chain analysis to assign weights to activities and context attributes of a complex activity as well as to build activity signatures based on transition and path probabilities. Our demo is intended to show how complex activities and associated atomic activities and context attributes can be described using an activity toolkit. The toolkit is used to take input from users available online and the results analysis of different complex activities can be viewed online in near real-time using the graphical user interface (GUI).
You can view a detailed demo video explaining the details of our Online Activity Toolkit for Context-driven Activity Theory by clicking here: Online Activity Toolkit Demo Video. Please view in “HD mode” for best results.
You can download the video by clicking: OAT4CDAT-demo-video (Right Click->Save Link As->Save)
Try OAT4CDAT yourself
To try OAT4CDAT, please click here: Online Activity Toolkit. We recommend using Mozilla Firefox or Google Chrome for best results.