Toward physical activity diary: motion recognition using simple acceleration features with mobile phones

ABSTRACT

In this paper, we perform physical motion recognition using mobile phones with built-in accelerometer sensors. Sensor data processing and smoothing techniques are discussed first to reduce the special noise present in phone-collected accelerometer data. We explore orientation-independent features extracted from vertical and horizonal components in acceleration as well as magnitudes of acceleration for six common physical activities, such as sitting, standing, walking, running, driving and bicycling. We find decision tree achieves the best performance among four commonly used static classifiers, while vertical and horizonal features have better recognition accuracy than magnitude features. Furthermore, a well-pruned decision tree with simple time domain features and less over-fitting on the training data can provide a usable model for inferencing a physical activity diary, refined by a similarity match from K-means clustering results and smoothed by an HMM-based Viterbi algorithm.

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Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by classification

    • Machine learning algorithms

      • Feature selection

    • Machine learning approaches

      • Classification and regression trees