Routine classification through sequence alignment

ABSTRACT

In this paper we draw a methodological connection between human routine classification and the sequence alignment problem in bioinformatics. We first observe that human days exhibit important time shifts and therefore align them for comparison prior to classification. Our technique is evaluated on bimodal data including GSM and Bluetooth information collected on mobile phones. The introduction of new alignment features is found to significantly improve the accuracy of routine classification.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering

General Terms:
Algorithms, Experimentation, Human Factors

Keywords:
human behaviour analysis, mobile phone, profile-HMM, sequence alignment