Recognition of Anaerobic based on Machine Learning using Smart Watch Sensor Data

@inproceedings{Cho2016RecognitionOA,
  title={Recognition of Anaerobic based on Machine Learning using Smart Watch Sensor Data},
  author={Soohyun Cho and Soowon Lee},
  year={2016}
}
In recent years, there has been an upsurge in research on smart watch technology. Existing research and commercial applications for machines that recognize user behaviors have involved measuring aerobic exercise using physical displacement metrics rather than anaerobic exercise, which involves recognizing and measuring user behaviors using signal processing techniques or other instruments. In this paper, we have created a prototypical machine learning algorithm to measure anaerobic exercise… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-7 of 7 references

A Survey of Research Trends on Smart Watch Interaction

H. Yoon, Lee, J-E., K.-T.
Journal of Korea Computer Congress, pp. 894-896 • 2015
View 1 Excerpt

Data Collection using Smart Watch and Machine Learning based Activity Condition Inference System

S. H. Kim, J. S. Choi, +3 authors H. G. Park
Journal of KICS, pp.34-35 • 2015
View 1 Excerpt

Real-time Activity and Posture Recognition with Combined Acceleration Sensor Data from Smartphone and Wearable Device

H. S. Lee, S. L. Lee
Journal of KIISE, vol. 41, no. 8, pp. 58-597 • 2014
View 1 Excerpt

A Novel Feature Selection Method for Output Coding based Multiclass SVM

Y. J. Lee, J. J. Lee
Journal of KMMS, vol. 16, no 7, pp. 795-801 • 2013
View 1 Excerpt

Personalized Activity Modeling and Real-time Activity Recognition based on Smartphone Multimodal Sensors

M. H. Han, S. L. Lee
Journal of KIISE, vol 40, no 6, pp. 33-341 • 2013
View 1 Excerpt

Similar Papers

Loading similar papers…