论文标题

Oreba:用于客观识别饮食行为和相关摄入量的数据集

OREBA: A Dataset for Objectively Recognizing Eating Behaviour and Associated Intake

论文作者

Rouast, Philipp V., Heydarian, Hamid, Adam, Marc T. P., Rollo, Megan E.

论文摘要

自动检测进气手势是自动饮食监测的关键要素。已将几种类型的传感器(包括惯性测量单元(IMU)和摄像机)用于此目的。通用的机器学习方法利用标记的传感器数据自动学习如何进行检测。一个特征,尤其是对于深度学习模型,是对大型数据集的需求。为了满足这种需求,我们收集了客观地识别饮食行为和相关摄入量(Oreba)数据集。 Oreba数据集旨在为对摄入手势检测感兴趣的研究人员提供在公共餐食期间记录的全面多传感器数据。包括两种情况,其中100名参与者用于离散的菜肴,有102名参与者共享菜肴,共有9069个进气口。可用的传感器数据由双手的同步额叶视频和IMU和IMU组成。我们报告了数据收集和注释的详细信息,以及传感器处理的详细信息。据报道,有关IMU和涉及深度学习模型的视频数据的研究结果为将来的研究提供了基准。具体而言,最佳的基线模型可用于使用视频的离散盘的$ f_1 $ = 0.853,使用惯性数据的共享菜肴$ f_1 $ = 0.852。

Automatic detection of intake gestures is a key element of automatic dietary monitoring. Several types of sensors, including inertial measurement units (IMU) and video cameras, have been used for this purpose. The common machine learning approaches make use of the labeled sensor data to automatically learn how to make detections. One characteristic, especially for deep learning models, is the need for large datasets. To meet this need, we collected the Objectively Recognizing Eating Behavior and Associated Intake (OREBA) dataset. The OREBA dataset aims to provide comprehensive multi-sensor data recorded during the course of communal meals for researchers interested in intake gesture detection. Two scenarios are included, with 100 participants for a discrete dish and 102 participants for a shared dish, totalling 9069 intake gestures. Available sensor data consists of synchronized frontal video and IMU with accelerometer and gyroscope for both hands. We report the details of data collection and annotation, as well as details of sensor processing. The results of studies on IMU and video data involving deep learning models are reported to provide a baseline for future research. Specifically, the best baseline models achieve performances of $F_1$ = 0.853 for the discrete dish using video and $F_1$ = 0.852 for the shared dish using inertial data.

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