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

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

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

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.

Original languageEnglish (US)
Article number5229323
Pages (from-to)1019-1030
Number of pages12
JournalIEEE Transactions on Information Technology in Biomedicine
Volume13
Issue number6
DOIs
StatePublished - Nov 2009
Externally publishedYes

Keywords

  • Biped locomotion
  • Body sensor networks
  • Hidden Markov models
  • Intelligent sensors

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'A method for extracting temporal parameters based on hidden markov models in body sensor networks with inertial sensors'. Together they form a unique fingerprint.

Cite this