Kyritsis Konstantinos, Diou Christos, Delopoulos Anastasios
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5511-5514. doi: 10.1109/EMBC.2018.8513627.
In this paper, we propose an end-to-end neural network (NN) architecture for detecting in-meal eating events (i.e., bites), using only a commercially available smartwatch. Our method combines convolutional and recurrent networks and is able to simultaneously learn intermediate data representations related to hand movements, as well as sequences of these movements that appear during eating. A promising F-score of 0.884 is achieved for detecting bites on a publicly available dataset with 10 subjects.
在本文中，我们提出了一种端到端神经网络（NN）架构，仅使用市售智能手表来检测用餐时的进食事件（即咬食动作）。我们的方法结合了卷积网络和循环网络，能够同时学习与手部动作相关的中间数据表示，以及进食过程中出现的这些动作序列。在一个有10名受试者的公开数据集上检测咬食动作时，获得了0.884的良好F分数。
