Hand Gesture Accelerometer and Gyroscope Dataset (HGAG-DATA)
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This dataset includes information from 43 healthy participants (26 males and 17 females). The participants encompass an extensive age range of 18 to 69 years and are classified by dominant hand, comprising 34 right-handed and 9 left-handed individuals. Furthermore, they are categorized according to physical activity levels, comprising 28 non-athletic individuals and 15 athletic individuals. All participants were equipped, trained, and directed to perform 11 essential gestures relevant in many circumstances and closely linked to daily life requirements. The motions encompass clapping, coin flipping, finger snapping, fist making, horizontal wrist extension, index finger flicking, index thumb tapping, shooting, thumbs up, wrist extension, and wrist flexion. The dataset comprises 23,650 six-dimensional gesture samples, captured at a rate of 550 samples per subject following 50 repetitions of each of the eleven motions (11x50x43 = 23,650). Each gesture sample consisted of six directional time series signals, incorporating two signals for each of the x, y, and z axes from the accelerometer and gyroscope sensors. This dataset comprises a total of 141,900 signals (23,650 x 6 = 141,900). Consequently, this can serve as a significant resource for handling extensive data sets, such as those utilized in the development of machine learning and deep learning models, and as a reference dataset, it facilitates model benchmarking. The data will be especially helpful for societies that work with biomedical signals when they are trying to recognize and classify hand gestures for use in human-computer interaction (HCI) tasks. The dataset has been uploaded and is accessible online in two distinct hierarchical configurations. The initial structure categorizes the data according to the gesture name, but the subsequent structure arranges it by subject number. This configuration enhances potential advantages and facilitates management, permitting the reassembly of data in many formats as required.
本数据集涵盖43名健康受试者的相关数据，其中男性26名，女性17名。受试者年龄跨度为18至69岁，覆盖范围较广，并按利手（dominant hand）类型进行分类，其中右利手34人，左利手9人。此外，受试者还按身体活动水平划分为28名非运动人群与15名运动人群。所有受试者均完成采集设备佩戴、动作培训，并被要求完成11种贴合日常场景与生活需求的典型手势动作，具体包括：拍手、抛硬币、弹手指、握拳、腕部水平伸展、食指弹击、食指拇指轻叩、射击手势、竖大拇指、腕部伸展、腕部屈曲。本数据集共包含23650个六维手势样本：每名受试者需完成11种手势各50次重复，采样速率为550个样本/受试者，样本总量计算公式为11×50×43=23650。每个手势样本包含6个带方向信息的时序信号，分别对应加速度计（accelerometer）与陀螺仪（gyroscope）传感器在x、y、z三轴的各2路信号。本数据集总信号量达141900路（23650×6=141900）。本数据集可作为处理大规模数据的重要资源，例如用于机器学习（machine learning）与深度学习（deep learning）模型的开发；同时可作为基准参考数据集，用于模型性能的基准测评。对于从事生物医学信号相关研究的团队而言，该数据集在人机交互（Human-Computer Interaction, HCI）场景下的手势识别与分类任务中具有极高的应用价值。本数据集已上线并以两种层级化结构在线开放获取：第一种结构按手势名称对数据进行分类，第二种结构则按受试者编号进行分组。该双结构设计可提升数据使用灵活性并便于管理，支持根据需求以多种格式重组数据。
