Vibration and IMU Sensing Human Activity Dataset

This dataset contains fine-grained human daily activity data collected by infrastructure vibration sensors and one on-wrist IMU sensor. This dataset is collected from six persons from two domestic homes, in total, there are 12 sub-datasets. For the naming, "p" means person and "l" means location. Each dataset has 11 columns, 1o of them stands for sensors' reading. * Due to the uploading platform, please ignore all files in the folder '__MACOSX', and files whose names start with '._'. These are computer system files, not parts of the shared dataset. ** If you are going to use this dataset for any publications, we will appreciate you to cite this dataset properly. ************************************************************ The following content is copied from README.txt in the compressed folder: ----------------------- Labels: Keyboard typing 1 Using mouse 2 Handwriting 3 Cutting vegetables 4 Stir-frying vegetables 5 Wiping the table 6 Sweeping floor 7 Using vacuum to vacuum floor: 8 Open and close drawer: 9 None Activity: 10 ----------------------- 11 Columns: 1: Activity label 2: Vibration sensor put on the Living Area floor 3: Vibration sensor put on the Living Area table 4: Vibration sensor put on the Studying Area floor 5: Vibration sensor put on the Studying Area desk 6, 7, 8: Accelerometer X,Y,Z 9, 10, 11: Gyroscope X,Y,Z ----------------------- All signals are zero-meaned. The vibration sensors' sampling rate is roughly around 6500Hz, and the IMU sensors' original sampling rate is roughly around 235Hz. ************************************************************ New in Version 2: - Added extracted features from IMU data and vibration data for reference. - IMU signal is applied with a sliding window of 1.5 seconds with 0.75 seconds overlapping, then the feature is extracted in each window. The feature's description can be found here: https://dl.acm.org/doi/abs/10.1145/3410530.3414320 - The vibration signal is applied with event detection to extract events in the vibration signal. For each event, we normalize it by its energy, then extract 10~490 Hz frequency amplitude as the feature. Disclaimer: Both event detection and feature extraction are empirical, we don't guarantee it is an optimal one.本数据集包含由基础设施振动传感器与腕部惯性测量单元（IMU）采集的细粒度人类日常活动数据。该数据集采集自两个家庭的六名受试者，总计包含12个子数据集。命名规则中，"p"代表受试者（person），"l"代表采集地点（location）。每个子数据集共11列，其中10列为传感器读数。 * 受上传平台限制，请忽略文件夹`__MACOSX`内的所有文件，以及名称以`._`开头的文件。此类文件为计算机系统生成的临时文件，不属于共享数据集的组成部分。** 若您将本数据集用于学术发表，请务必正确引用本数据集，我们将不胜感激。 ************************************************************ 以下内容摘自压缩包内的README.txt文件： ----------------------- 活动标签： 键盘打字 1 使用鼠标 2 手写 3 切菜 4 炒菜 5 擦拭桌面 6 扫地 7 使用吸尘器清洁地面 8 开合抽屉 9 无活动 10 ----------------------- 11列数据说明： 1: 活动标签 2: 铺设于客厅地面的振动传感器读数 3: 铺设于客厅桌面的振动传感器读数 4: 铺设于学习区地面的振动传感器读数 5: 铺设于学习区书桌的振动传感器读数 6、7、8：加速度计X、Y、Z轴数据 9、10、11：陀螺仪X、Y、Z轴数据 ----------------------- 所有信号均已完成零均值化处理。振动传感器的采样率约为6500Hz，惯性测量单元（IMU）的原始采样率约为235Hz。 ************************************************************ 版本2新增内容： - 新增从IMU数据与振动数据中提取的特征以供参考。 - 对IMU信号采用1.5秒滑动窗口、0.75秒重叠率进行分窗处理，并在每个窗口内提取特征。特征的详细说明可参见：https://dl.acm.org/doi/abs/10.1145/3410530.3414320 - 对振动信号进行事件检测以提取振动信号中的事件片段。针对每个事件，以其能量进行归一化处理，并提取10~490Hz频段的频率幅值作为特征。 免责声明：事件检测与特征提取均为经验性方法，我们无法保证其为最优方案。