Analog Gated Recurrent Neural Network for Detecting Chewing Events
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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 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.
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