ADL Classification Based on Autocorrelation Function of Inertial Signals

@article{Gomaa2017ADLCB,
  title={ADL Classification Based on Autocorrelation Function of Inertial Signals},
  author={Walid Gomaa and Reda Elbasiony and Sara Ashry Mohammed},
  journal={2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  year={2017},
  pages={833-837}
}
Recognition of human activities is one of the most promising research areas in artificial intelligence. This has come along with the technological advancement in sensing technologies as well as the high demand for applications that are mobile, context-aware, and real-time. In this paper, we use a smart watch to collect sensory data for 14 ADL activities. We collect three types of sensory signals: acceleration, angular velocity, and rotation displacement; each is a tri-axial signal. From each… CONTINUE READING

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Key Quantitative Results

  • The joint use of acceleration with angular velocity has achieved the best performance in prediction accuracy which reaches about 80% for the whole set of 14 activities.1.

Citations

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SHOWING 1-2 OF 2 CITATIONS

Novel Approaches to Activity Recognition Based on Vector Autoregression and Wavelet Transforms

  • 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)
  • 2018
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CITES METHODS & BACKGROUND

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