Personalized Active Learning for Activity Classification Using Wireless Wearable Sensors

Jie Xu, Linqi Song, James Y. Xu, Gregory J. Pottie, Mihaela Van Der Schaar

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Enabling accurate and low-cost classification of a range of motion activities is important for numerous applications, ranging from disease treatment and in-community rehabilitation of patients to athlete training. This paper proposes a novel contextual online learning method for activity classification based on data captured by low-cost, body-worn inertial sensors, and smartphones. The proposed method is able to address the unique challenges arising in enabling online, personalized and adaptive activity classification without requiring training phase from the individual. Another key challenge of activity classification is that the labels may change over time, as the data as well as the activity to be monitored evolve continuously, and the true label is often costly and difficult to obtain. The proposed algorithm is able to actively learn when to ask for the true label by assessing the benefits and costs of obtaining them. We rigorously characterize the performance of the proposed learning algorithm and Our experiments show that the proposed algorithm outperforms existing algorithms.

Original languageEnglish (US)
Article number7452393
Pages (from-to)865-876
Number of pages12
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number5
StatePublished - Aug 2016


  • Activity classification
  • active learning
  • context-aware
  • multi-armed bandits
  • online learning

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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