走向社会可接受的食物类型识别

Junjie Wang, Jiexiong Guan, Y. A. Hong, Hong Xue, Shuangquan Wang, Zhenming Liu, Bin Ren, Gang Zhou
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引用次数: 0

摘要

食物类型自动识别是饮食监测的一项重要任务。它可以帮助医疗专业人员识别用户的食物含量,估计能量摄入量,并设计个性化的干预模型,以预防许多慢性疾病,如肥胖和心脏病。各种可穿戴和移动设备被用作食品类型识别的平台。然而,它们都没有在我们的日常生活中广泛使用,同时也没有被社会所接受,可以持续佩戴。在本文中,我们提出了一种食物类型识别方法,利用苹果公司设计的一副广泛使用的无线入耳式耳机Airpods Pro来识别20种不同类型的食物。据我们所知,我们是第一个使用这种社会认可的商业产品来识别食物类型的公司。音频和运动传感器数据是从Airpods Pro收集的。然后提取并选择135个具有代表性的特征,使用lightGBM算法构建识别模型。真实世界的数据收集进行全面评估的性能提出的方法为七个人类受试者。结果表明,十重交叉验证的平均f1得分为94.4%,自评测试的平均f1得分为96.0%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Socially Acceptable Food Type Recognition
Automatic food type recognition is an essential task of dietary monitoring. It helps medical professionals recognize a user's food contents, estimate the amount of energy intake, and design a personalized intervention model to prevent many chronic diseases, such as obesity and heart disease. Various wearable and mobile devices are utilized as platforms for food type recognition. However, none of them has been widely used in our daily lives and, at the same time, socially acceptable enough for continuous wear. In this paper, we propose a food type recognition method that takes advantage of Airpods Pro, a pair of widely used wireless in-ear headphones designed by Apple, to recognize 20 different types of food. As far as we know, we are the first to use this socially acceptable commercial product to recognize food types. Audio and motion sensor data are collected from Airpods Pro. Then 135 representative features are extracted and selected to construct the recognition model using the lightGBM algorithm. A real-world data collection is conducted to comprehensively evaluate the performance of the proposed method for seven human subjects. The results show that the average f1-score reaches 94.4% for the ten-fold cross-validation test and 96.0% for the self-evaluation test.
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