A Reliable and Reconfigurable Signal Processing Framework for Estimation of Metabolic Equivalent of Task in Wearable Sensors

@article{Alinia2016ARA,
  title={A Reliable and Reconfigurable Signal Processing Framework for Estimation of Metabolic Equivalent of Task in Wearable Sensors},
  author={Parastoo Alinia and Ramyar Saeedi and Ramin Fallahzadeh and Seyed Ali Rokni and Hassan Ghasemzadeh},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  year={2016},
  volume={10},
  pages={842-853}
}
Wearable motion sensors are widely used to estimate metabolic equivalent of task (MET) values associated with physical activities. However, one major obstacle in widespread adoption of current wearables is that any changes in configuration of the network requires new data collection and re-training of the underlying signal processing algorithms. For any wearable-based MET estimation framework to be considered a viable platform, it needs to be reconfigurable, reliable, and power-efficient. In… CONTINUE READING
Highly Cited
This paper has 18 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 10 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 46 references

Similar Papers

Loading similar papers…