Analog Gated Recurrent Neural Network for Detecting Chewing Events

Authors

Kofi Odame, Dartmouth CollegeFollow
Maria Nyamukuru, Dartmouth CollegeFollow
Mohsen ShahghasemiFollow
Shengjie Bi, MetaFollow
David KotzFollow

Author ORCID Identifier

Kofi Odame: https://orcid.org/0000-0002-8519-2392

Maria Nyamukuru: https://orcid.org/0000-0002-7748-5013

Mohsen Shahghasemi: https://orcid.org/0000-0003-0638-516X

David Kotz: https://orcid.org/0000-0001-7411-2783

Document Type

Article

Publication Date

12-2022

Publication Title

IEEE Transactions on Biomedical Circuits and Systems

Department

Thayer School of Engineering

Additional Department

Department of Computer Science

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 μm 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 μW 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 μW of power.

DOI

10.1109/TBCAS.2022.3218889

Original Citation

Kofi Odame, Maria Nyamukuru, Mohsen Shahghasemi, Shengjie Bi, and David Kotz. Analog Gated Recurrent Neural Network for Detecting Chewing Events. IEEE Transactions on Biomedical Circuits and Systems, volume 16, number 6, pages 1106–1115. IEEE, December 2022. doi:10.1109/TBCAS.2022.3218889.

Dartmouth Digital Commons Citation

Odame, Kofi; Nyamukuru, Maria; Shahghasemi, Mohsen; Bi, Shengjie; and Kotz, David, "Analog Gated Recurrent Neural Network for Detecting Chewing Events" (2022). Dartmouth Scholarship. 4310.
https://digitalcommons.dartmouth.edu/facoa/4310