Mobile Based Prompted Labeling of Large Scale Activity Data

Ian Cleland, Manhyung Han, Chris D. Nugent, Hosung Lee, Shuai Zhang, Sally McClean, Sungyoung Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)
101 Downloads (Pure)


This paper describes the use of a prompted labeling solution to obtain class labels for user activity and context information on a mobile device. Based on the output from an activity recognition module, the prompt labeling module polls for class transitions from any of the activities (e.g. walking, running) to the standing still activity. Once a transition has been detected the system prompts the user, through the provision of a message on the mobile phone, to provide a label for the last activity that was carried out. This label, along with the raw sensor data is then stored locally prior to being uploaded to cloud storage. The paper provides technical details of how and when the system prompts the user for an activity label and discusses the information that can be gleaned from sensor data. This system allows for activity and context information to be collected on a large scale. Data can then be used within new opportunities in data mining and modeling of user context for a variety of applications.
Original languageEnglish
Title of host publicationUnknown Host Publication
Number of pages8
ISBN (Print)978-3-319-03092-0
Publication statusPublished (in print/issue) - 2 Dec 2013
Event5th International Work-Conference on Ambient Assisted Living - Carrillo, Costa Rica
Duration: 2 Dec 2013 → …


Conference5th International Work-Conference on Ambient Assisted Living
Period2/12/13 → …


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