Nonparametric discovery of movement patterns from accelerometer signals

Thuong Nguyen, Sunil Gupta, Svetha Venkatesh, Dinh Phung

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)


Monitoring daily physical activity plays an important role in disease prevention and intervention. This paper proposes an approach to monitor the body movement intensity levels from accelerometer data. We collect the data using the accelerometer in a realistic setting without any supervision. The ground-truth of activities is provided by the participants themselves using an experience sampling application running on their mobile phones. We compute a novel feature that has a strong correlation with the movement intensity. We use the hierarchical Dirichlet process (HDP) model to detect the activity levels from this feature. Consisting of Bayesian nonparametric priors over the parameters the model can infer the number of levels automatically. By demonstrating the approach on the publicly available USC-HAD dataset that includes ground-truth activity labels, we show a strong correlation between the discovered activity levels and the movement intensity of the activities. This correlation is further confirmed using our newly collected dataset. We further use the extracted patterns as features for clustering and classifying the activity sequences to improve performance.

Original languageEnglish
Pages (from-to)52-58
Number of pages7
JournalPattern Recognition Letters
Publication statusPublished - Nov 2016
Externally publishedYes


  • Accelerometer
  • Activity recognition
  • Bayesian nonparametric
  • Dirichlet process
  • Movement intensity

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