# Analog Gated Recurrent Neural Network for Detecting Chewing Events

arXiv: https://arxiv.org/abs/2208.01201 · HTML: https://mlsanity.com/paper/2208.01201/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [An Optimized Recurrent Unit for Ultra-Low-Power Keyword Spotting](https://mlsanity.com/paper/1902.05026/) (2019)
- [Tiny Eats: Eating Detection on a Microcontroller](https://mlsanity.com/paper/2003.06699/) (2020)
- [Learning in Memristive Neural Network Architectures using Analog Backpropagation Circuits](https://mlsanity.com/paper/1808.10631/) (2018)
- [Memristive LSTM network hardware architecture for time-series predictive modeling problem](https://mlsanity.com/paper/1809.03119/) (2018)
- [Birhythmic Analog Circuit Maze: A Nonlinear Neurostimulation Testbed](https://mlsanity.com/paper/2004.10658/) (2020)

## Abstract (arXiv)

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 um 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 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 uW 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 18.8uW of power.