Corpus ID: 54488587

The Impact of Quantity of Training Data on Recognition of Eating Gestures

@article{Shen2018TheIO,
  title={The Impact of Quantity of Training Data on Recognition of Eating Gestures},
  author={Y. Shen and E. Muth and A. Hoover},
  journal={ArXiv},
  year={2018},
  volume={abs/1812.04513}
}
  • Y. Shen, E. Muth, A. Hoover
  • Published 2018
  • Computer Science, Mathematics
  • ArXiv
  • This paper considers the problem of recognizing eating gestures by tracking wrist motion. Eating gestures can have large variability in motion depending on the subject, utensil, and type of food or beverage being consumed. Previous works have shown viable proofs-of-concept of recognizing eating gestures in laboratory settings with small numbers of subjects and food types, but it is unclear how well these methods would work if tested on a larger population in natural settings. As more subjects… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 29 REFERENCES
    A Tutorial on Hidden Markov Models and Selected Applications
    • 23,446
    • Highly Influential
    • PDF
    Introduction to information retrieval
    • 9,366
    A practical approach for recognizing eating moments with wrist-mounted inertial sensing
    • 177
    • PDF
    Detection of eating and drinking arm gestures using inertial body-worn sensors
    • 167
    • PDF
    Regression modelling strategies for improved prognostic prediction.
    • 1,313
    Assessing the Accuracy of a Wrist Motion Tracking Method for Counting Bites Across Demographic and Food Variables
    • 33
    • PDF