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Bayesian recognition of motion related activities with inertial sensors

Published: 26 September 2010 Publication History

Abstract

This work presents the design and evaluation of an activity recognition system for seven important motion related activities. The only sensor used is an Inertial Measurement Unit (IMU) worn on the belt.
For classification, we applied Bayesian techniques, based on relevant features of the IMU raw data which are calculated in real time. Based on a complete labelled data set, i.e. supervised by an observing human judge, a K2 learning algorithm by Cooper and Herskovits was used to construct the Bayesian Network (BN) of the features.
Our comparison of dynamic and static inference algorithms, based on the evaluation of the labelled data sets recorded from 16 male and female subjects show that a Hidden Markov Model (HMM) based on a learnt BN provides the best results.

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References

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cover image ACM Conferences
UbiComp '10 Adjunct: Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Adjunct
September 2010
203 pages
ISBN:9781450302838
DOI:10.1145/1864431

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  • University of Florida: University of Florida

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2010

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Author Tags

  1. activity recognition
  2. bayesian networks
  3. context inference
  4. inertial navigation

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Ubicomp '10
Ubicomp '10: The 2010 ACM Conference on Ubiquitous Computing
September 26 - 29, 2010
Copenhagen, Denmark

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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