UMBC ebiquity

XPod: A Human Activity Aware Learning Mobile Music Player

Authors: Sandor Dornbush, Jesse English, Tim Oates, Zary Segall, and Anupam Joshi

Book Title: Proceedings of the Workshop on Ambient Intelligence, 20th International Joint Conference on Artificial Intelligence (IJCAI-2007)

Date: January 08, 2007

Abstract: The XPod system, presented in this paper, aims to integrate awareness of human activity and musical preferences to produce an adaptive system that plays the contextually correct music. The XPod project introduces a “smart” music player that learns its user’s preferences and activity, and tailors its music selections accordingly. We are using a BodyMedia device that has been shown to accurately measure a user’s physiological state. The device is able to monitor a number of variables to determine its user’s levels of activity, motion and physical state so that it may predict what music is appropriate at that point. The XPod user trains the player to understand what music is preferred and under what conditions. After training, the XPod, using various machine-learning techniques, is able to predict the desirability of a song, given the user’s physical state.

Type: InProceedings

Edition: 2007

Tags: mp3, music, learning, music, mobile computing, context, wearable computing

Google Scholar: ImEOVyHnkj8J

Number of Google Scholar citations: 13 [show citations]

Number of downloads: 3417


Available for download as

size: 170862 bytes

Related Projects:

Past Project




  1. (Publication) XPod: A Human Activity Aware Learning Mobile Music Player is a related publication of project (Project) XPod