This paper describes a metric-based model for event segmentation of sensor data recorded by a mobile phone worn around subjects’ necks during their daily life. More specifically, we aim at detecting human daily event boundaries by analysing the recorded triaxial accelerometer signals and images sequence (lifelog data). In the experiments, different signal representations and three boundary detection models are evaluated on a corpus of 2 subjects over total 24 days. The contribution of this paper is three-fold. First, we find that using accelerometer signals can provide much more reliable and significantly better performance than using image signals with MPEG-7 low level features. Second, the models using the accelerometer data based on the world’s coordinates system can provide equally or even much better performance than using the accelerometer data based on the device’s coordinates system. Finally, our proposed model has a better performance than the state of the art system [1].


Triaxial accelerometer Event segmentation Lifelog data 


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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Yuwen Zhuang
    • 1
  • Mikhail Belkin
    • 1
  • Simon Dennis
    • 2
  1. 1.Department of Computer Science and EngineeringOhio State UniversityColumbusUSA
  2. 2.Department of PsychologyOhio State UniversityColumbusUSA

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