Date of Original Version

2007

Type

Conference Proceeding

Abstract or Description

Time management is an important aspect of a successful professional life. In order to have a better understanding of where our time goes, we propose a system that summarizes the user’s daily activity (e.g. sleeping, walking, working on the computer, talking, ...) using all-day multimodal data recordings. Two main novelties are proposed: • A system that combines both physical and contextual awareness hardware and software. It records synchronized audio, video, body sensors, GPS and computer monitoring data. • A semi-supervised temporal clustering (SSTC) algorithm that accurately and efficiently groups large amounts of multimodal data into different activities. The effectiveness and accuracy of our SSTC is demonstrated in synthetic and real examples of activity segmentation from multimodal data gathered over long periods of time.

Comments

"©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."

Included in

Robotics Commons

Share

COinS