Physical activity recognition using multiple sensors embedded in a wearable device

Yunyoung Nam, Seungmin Rho, Chulung Lee

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


In this article, we present a wearable intelligence device for activity monitoring applications. We developed and evaluated algorithms to recognize physical activities from data acquired using a 3-axis accelerometer with a single camera worn on a body. The recognition process is performed in two steps: at first the features for defining a human activity are measured by the 3-axis accelerometer sensor and the image sensor embedded in a wearable device. Then, the physical activity corresponding to the measured features is determined by applying the SVM classifier. The 3-axis accelerometer sensor computes the correlation between axes and the magnitude of the FFT for other features of an activity. Acceleration data is classified into nine activity labels. Through the image sensor, multiple optical flow vectors computed on each grid image patch are extracted as features for defining an activity. In the experiments, we showed that an overall accuracy rate of activity recognition based our method was 92.78%.

Original languageEnglish
Article number26
JournalTransactions on Embedded Computing Systems
Issue number2
Publication statusPublished - 2013 Feb


  • Accelerometer
  • Human activity recognition
  • SVM
  • Ubiquitous
  • Wearable computing

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture


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