Analog Gated Recurrent Neural Network for Detecting Chewing Events

TL;DR

This paper introduces an analog gated recurrent neural network integrated circuit designed to detect chewing events with high accuracy and low power consumption, enabling efficient monitoring of eating behavior.

Contribution

The paper presents a novel analog gated recurrent neural network implemented as a custom chip for real-time chewing detection, combining high accuracy with ultra-low power usage.

Findings

Achieved 91% recall and 94% F1-score in chewing detection.

Operates at 1.1 microWatts power consumption.

Successfully identified chewing events at 24-second resolution.

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 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.