InGesture Dataset

The InGesture dataset provides high-resolution (200 Hz) inertial sensor data for hand gesture recognition, focusing on the challenge of distinguishing fluid intake from seven other kinematically similar gestures. Data were collected from 50 participants (34 male, 16 female, aged 18-67) across 65 recording sessions. An inertial sensor (WT901BLECL5 IMU) was placed on each participant's dominant wrist, and gestures were annotated in real-time with a synchronized mobile app to ensure high temporal accuracy. The full dataset contains de labeled gesture instances. To facilitate use, the data is provided in two complementary formats: 1. Continuous Recordings: CSV files containing the full recording of each session (~10 minutes), ideal for segmentation and sequence modeling tasks. Columns: timestamp, accX/Y/Z (accelerometer), asX/Y/Z (gyroscope), and label (gesture code). 2. Segmented Gestures: Individual CSV files, where each file represents a single, pre-segmented gesture instance, ready for use in classification models. Columns: timestamp, x/y/z (accelerometer), gx/gy/gz (gyroscope). The filename indicates the gesture (e.g., fluid_intake_1_800.csv). Gesture Classes (Labels): 0: Free Condition / Other 1: Fluid Intake 2: Answering Phone 3: Scratching Head 4: Passing Hand over Face 5: Adjusting Glasses 6: Holding Chin 7: Stretching with Hands behind Neck Additional Resources: To accelerate analysis, the repository includes: two Jupyter Notebook with Python code for loading, processing, and visualizing the data and classify examples. A detailed metadata file with participant demographics (age, sex, height, weight) and session details (e.g., container type used), allowing for robust, stratified analysis.InGesture数据集提供了用于手势识别的高分辨率（200 Hz）惯性传感器数据，其核心研究难点在于区分饮水动作与其余7种运动学特征相似的手势。数据采集覆盖50名受试者（男性34名、女性16名，年龄区间18至67岁），共计65次录制场次。 每名受试者的惯用手腕佩戴了WT901BLECL5型惯性测量单元（IMU），并通过同步移动应用实时标注手势，以保障极高的时间对齐精度。完整数据集包含已标注的手势样本实例。 为便于使用，数据集以两种互补格式提供： 1. 连续录制数据： 包含每次完整录制（时长约10分钟）的CSV文件，适用于分段处理与序列建模任务。 字段包括：时间戳、accX/Y/Z（加速度计）、asX/Y/Z（陀螺仪）以及标签（手势编码）。 2. 分段手势数据： 单个独立CSV文件，每个文件对应一个已预先完成分段的手势样本，可直接用于分类模型训练。 字段包括：时间戳、x/y/z（加速度计）、gx/gy/gz（陀螺仪）。文件名会标注手势类型，例如fluid_intake_1_800.csv。 手势类别（标签）： 0：空闲状态/其他动作 1：饮水动作 2：接打电话 3：挠头 4：手拂过面部 5：调整眼镜 6：托腮 7：双手置于颈后拉伸 附加资源： 为加快分析效率，本数据集仓库还包含两份用于数据加载、处理、可视化以及分类示例的Jupyter Notebook； 一份详细的元数据文件，涵盖受试者人口统计学信息（年龄、性别、身高、体重）与录制场次详情（例如所用容器类型），可支持严谨的分层分析。