BibTeX for papers by David Kotz; for complete/updated list see
https://www.cs.dartmouth.edu/~kotz/research/papers.html

@Article{odame:chewing,
  author =        {Kofi Odame and Maria Nyamukuru and Mohsen Shahghasemi and Shengjie Bi and David Kotz},
  title =         {{Analog Gated Recurrent Neural Network for Detecting Chewing Events}},
  journal =       {IEEE Transactions on Biomedical Circuits and Systems},
  year =          2022,
  month =         {December},
  volume =        16,
  number =        6,
  pages =         {1106--1115},
  publisher =     {IEEE},
  copyright =     {IEEE},
  DOI =           {10.1109/TBCAS.2022.3218889},
  URL =           {https://www.cs.dartmouth.edu/~kotz/research/odame-chewing/index.html},
  abstract =      {We present a novel gated recurrent neural network to detect when a person is chewing on food. We implemented the neural network as a custom analog integrated circuit in a 0.18 {$\mu$}m CMOS technology. The neural network was trained on 6.4 hours of data collected from a contact microphone that was mounted on volunteers' mastoid bones. When tested on 1.6 hours of previously-unseen data, the analog neural network identified chewing events at a 24-second time resolution. It achieved a recall of 91\% and an F1-score of 94\% while consuming 1.1 {$\mu$}W of power. A system for detecting whole eating episodes--- like meals and snacks--- that is based on the novel analog neural network consumes an estimated 18.8 {$\mu$}W of power.},
}