A method for extracting temporal parameters based on hidden Markov models in body sensor networks with inertial sensors

  • Authors:

  • Eric Guenterberg;Allen Y. Yang;Hassan Ghasemzadeh;Roozbeh Jafari;Ruzena Bajcsy;S. Shankar Sastry

  • Affiliations:

  • Embedded Systems and Signal Processing Laboratory, Department of Electrical Engineering, University of Texas at Dallas, Dallas, TX;Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA;Embedded Systems and Signal Processing Laboratory, Department of Electrical Engineering, University of Texas at Dallas, Dallas, TX;Embedded Systems and Signal Processing Laboratory, Department of Electrical Engineering, University of Texas at Dallas, Dallas, TX;Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA;Center for Information Technology in the Interests of Society, University of California, Berkeley, CA

  • Venue:
  • IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
  • Year:
  • 2009

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Abstract

Human movement models often divide movements into parts. In walking, the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into sections based on the primary direction of motion. These parts are often divided based on key events, also called temporal parameters. When analyzing a movement, it is important to correctly locate these key events, and so automated techniques are needed. There exist many methods for dividing specific actions using data from specific sensors, but for new sensors or sensing positions, new techniques must be developed. We introduce a generic method for temporal parameter extraction called the hidden Markov event model based on hidden Markov models. Our method constrains the state structure to facilitate precise location of key events. This method can be quickly adapted to new movements and new sensors/sensor placements. Furthermore, it generalizes well to subjects not used for training. A multiobjective optimization technique using genetic algorithms is applied to decrease error and increase cross-subject generalizability. Further, collaborative techniques are explored. We validate this method on a walking dataset by using inertial sensors placed on various locations on a human body. Our technique is designed to be computationally complex for training, but computationally simple at runtime to allow deployment on resource-constrained sensor nodes.