Multimodal Fine-grained 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. 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. ** For usage and questions, please contact zhu42 [AT] ucmerced [DOT] edu *** If you are going to use this dataset for any publications, we will appreciate you to cite this dataset properly. ************************************************************ For more information, please refer to this paper: title: VMA: Domain Variance- and Modality-Aware Model Transfer for Fine-Grained Occupant Activity Recognition,
doi: doi.org/10.1109/IPSN54338.2022.00028
************************************************************ The following content is copied from README.txt: ----------------------- 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.

本数据集包含由基础设施振动传感器与腕部穿戴式惯性测量单元（IMU）采集的细粒度人类日常活动数据。该数据集采集自两户家庭的六名参与者，总计包含12个子数据集。每个子数据集均包含11列数据，其中10列为传感器读数。
* 由于上传平台的限制，请忽略文件夹`__MACOSX`内的所有文件，以及文件名以`._`开头的文件，此类均为计算机系统文件，不属于共享数据集的组成部分。
** 若需使用该数据集或有相关疑问，请联系邮箱zhu42[AT]ucmerced[DOT]edu。
*** 若将本数据集用于学术发表，请您务必正确引用本数据集。
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更多信息请参阅以下论文：
标题：VMA：面向细粒度居住者活动识别的域方差与模态感知模型迁移（VMA: Domain Variance- and Modality-Aware Model Transfer for Fine-Grained Occupant Activity Recognition）
DOI：doi.org/10.1109/IPSN54338.2022.00028
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以下内容摘自README.txt：
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标签：
1. 键盘打字
2. 使用鼠标
3. 手写书写
4. 切菜
5. 炒菜
6. 擦拭桌面
7. 扫地
8. 使用吸尘器清洁地面
9. 开合抽屉
10. 无活动
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11列说明：
1. 活动标签
2. 铺设于客厅地面的振动传感器数据
3. 铺设于客厅桌面的振动传感器数据
4. 铺设于学习区地面的振动传感器数据
5. 铺设于学习区书桌的振动传感器数据
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。

搜集汇总

背景与挑战

背景概述

该数据集是一个多模态细粒度人类活动数据集，通过基础设施振动传感器和手腕IMU传感器收集了来自两个家庭六个人的日常活动数据，共包含12个子数据集和10类活动标签（如键盘打字、使用鼠标等）。数据具有高采样率（振动传感器约6500Hz，IMU传感器约235Hz），适用于细粒度活动识别研究，并支持跨域和跨模态的模型迁移应用。