论文标题
用于检测咀嚼事件的模拟门控复发性神经网络
Analog Gated Recurrent Neural Network for Detecting Chewing Events
论文作者
论文摘要
我们提出了一个新颖的封闭式复发性神经网络，以检测一个人何时咀嚼食物。我们在0.18 UM CMOS技术中将神经网络作为自定义模拟集成电路实现。对神经网络进行了6.4小时的数据，该数据是从安装在志愿者的乳突骨骼上的接触麦克风中收集的。当对1.6个小时的以前未见数据进行测试时，神经网络以24秒的分辨率确定了咀嚼事件。它的召回率为91％，F1得分为94％，同时消耗了1.1 UW的功率。一种用于检测整个饮食情节（例如餐和小吃）的系统，该系统基于新颖的模拟神经网络的消耗估计为18.8uw的力量。
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.
