Building activity definitions to recognize complex activities using an online activity toolkit
2012 (English)In: IEEE 13th International Conference on Mobile Data Management, MDM 2012, Piscataway, NJ: IEEE Communications Society, 2012, 344-347 p.Conference paper (Refereed)
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).
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2012. 344-347 p.
Media and Communication Technology
Research subject Mobile and Pervasive Computing
IdentifiersURN: urn:nbn:se:ltu:diva-34255DOI: 10.1109/MDM.2012.73Local ID: 866f52de-e2af-4995-b4c5-97390a8da692ISBN: 9780769547138 (print)OAI: oai:DiVA.org:ltu-34255DiVA: diva2:1007505
International Conference on Mobile Data Management : 23/07/2012 - 26/07/2012
Godkänd; 2012; 20121219 (andbra)2016-09-302016-09-30Bibliographically approved