WIMID: Wrist IMU Meals and Intakes Dataset

WIMID (Wrist IMU Meals and Intakes Dataset) is a free-living wearable sensor dataset intended for machine-learning research on eating behavior recognition. It consists of triaxial accelerometer and gyroscope recordings acquired from a smartwatch worn on the dominant hand during participants’ everyday activities, covering both eating and non-eating periods. The dataset was recorded from 11 participants and spans approximately 528 hours of data, including 159 meals. For 75 meals, participants additionally provided self-reported counts of dietary intakes. Information about how dietary intakes were performed (e.g., by hand, fork, spoon, or chopsticks) is also provided. Liquid consumption is excluded from the definition of eating. IMU data were recorded at a nominal sampling rate of 100 Hz. Due to minor variations in the actual recording frequency, signals were linearly interpolated to 100 Hz and subsequently smoothed using median filtering. The dataset provides the resulting preprocessed IMU recordings. The data were collected by researchers at the Jožef Stefan Institute, Slovenia.WIMID（腕部惯性测量单元饮食与摄入数据集，Wrist IMU Meals and Intakes Dataset）是一款面向饮食行为识别机器学习研究的自由活动场景穿戴式传感器数据集。该数据集包含参与者日常活动期间佩戴于利手腕部的智能手表采集的三轴加速度计与陀螺仪记录数据，覆盖进食与非进食时段。 本数据集采集自11名参与者，总数据时长约528小时，涵盖159次进食行为。其中75次进食行为附带参与者自行报告的饮食摄入量计数信息，同时还提供了进食方式（如徒手、叉子、勺子或筷子）的相关数据。液体摄入未被纳入进食的定义范畴。 IMU（惯性测量单元，Inertial Measurement Unit）数据的标称采样率为100 Hz。由于实际采集频率存在小幅波动，所有信号均被线性插值至100 Hz，随后通过中值滤波进行平滑处理。本数据集提供经预处理后的最终IMU记录数据。 本数据集由斯洛文尼亚约瑟夫·斯特凡研究所的研究人员采集完成。