A Human Activity Aware Learning Mobile Music Player

  • Authors:
  • Sandor Dornbush;Anupam Joshi;Zary Segall;Tim Oates

  • Affiliations:
  • Computer Science Department, University of Maryland Baltimore County, USA;Computer Science Department, University of Maryland Baltimore County, USA;Computer Science Department, University of Maryland Baltimore County, USA;Computer Science Department, University of Maryland Baltimore County, USA

  • Venue:
  • Proceedings of the 2007 conference on Advances in Ambient Intelligence
  • Year:
  • 2007

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Abstract

The XPod system aims to integrate awareness of human activity and multimedia preferences to produce an adaptive system that plays contextually appropriate media. The XPod project introduces a “smart” music player that learns its user's preferences and activity, and tailors its music selections accordingly. We have experimented with various physiological sensing platforms to measure a user's physiological state. These devices are able to monitor a number of variables to determine the user's levels of activity and motion to predict what music is appropriate at that time. The XPod user trains the player to understand what music is preferred and under what conditions. After training, XPod can predict the desirability of a song given the user's physical state. XPod learns a users listening preferences from the interaction with the user and from collaborative filtering