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）是一款面向进食行为识别机器学习研究的日常自由活动场景穿戴式传感器数据集。该数据集采集了受试者在日常活动中，于惯用手佩戴智能手表时产生的三轴加速度计与陀螺仪记录数据，覆盖进食与非进食两类时段。 该数据集的采集样本为11名受试者，总数据时长约528小时，包含159次进食事件。其中75次进食事件中，受试者额外提供了自我报告的饮食摄入次数。数据集同时记录了进食方式（如徒手、餐叉、餐勺或筷子）。需注意，液体摄入不计入本次数据集定义的进食范畴。 惯性测量单元（Inertial Measurement Unit, IMU）数据的标称采样率为100Hz。由于实际录制频率存在微小偏差，所有信号均被线性插值至100Hz，并通过中值滤波进行平滑处理。本数据集提供预处理后的最终IMU录制数据。 该数据集由斯洛文尼亚约瑟夫·斯特凡研究所（Jožef Stefan Institute）的研究人员采集。