自回数据集包含9个活动类的数据 6项步行活动,3项静坐活动

Sadiq Sani, Nirmalie Wiratunga, Kay Cooper Robert Gordon University Aberdeen, UK Data Set Information: The SELFBACK dataset contains data of 9 activity classes; 6 ambulatory activities and 3 sedentary activities, performed by 33 participants. Data are recorded with two tri-axial accelerometers sampling at 100Hz, mounted on the dominant side wrist and the thigh of the participant. **Application** The dataset can be used for human activity recognition by developing algorithms for pre-processing, feature extraction, sensor fusion, segmentation and classification. ** Data collection method ** Each participant performed an activity for approximately 3 minutes. ** Sensors** Axivity AX3 3-Axis Logging Accelerometer - sampling frequency -- 100Hz - range -- 8g ** Activity Classes** - Walking Upstairs - Walking Downstairs - Walking in slow pace - Walking in medium pace - Walking in fast pace - Jogging - Standing - Sitting - Lying ** Data folder ** SELFBACK dataset has three folders, two folders one for each sensor modality named "w" for wrist and "t" for thigh and an additional folder where two sensor modalities are merged using timestamp named "wt" for wrist and thigh. Inside "w" and "t" folders, 9 folders can be found, one for each activity class, and inside, there are 33 files, one file for each participant. Inside "wt" folder, there are 297(33 X 9) files where the file name indicates the person and the activity. Attribute Information: The 4 columns in the files in t and w folder is organized as follows: 1 -- timestamp 2 -- x value 3 -- y value 4 -- z value Min value = -8 Max value = +8 The 6 columns in the files in wt folder is organized as follows: 1 -- wrist x value 2 -- wrist y value 3 -- wrist z value 4 -- thigh x value 5 -- thigh y value 6 -- thigh z value Min value = -8 Max value = +8 Relevant Papers: - Sani, S., Wiratunga, N., Massie, S., & Cooper, K. (2016, December). SELFBACK--activity recognition for self-management of low back pain. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 281-294). Springer, Cham. - Sani, S., Massie, S., Wiratunga, N., & Cooper, K. (2017, August). Learning deep and shallow features for human activity recognition. In International Conference on Knowledge Science, Engineering and Management (pp. 469-482). Springer, Cham. - Sani, S., Wiratunga, N., Massie, S., & Cooper, K. (2017, June). kNN sampling for personalised human activity recognition. In International conference on case- based reasoning (pp. 330-344). Springer, Cham. - Sani S, Wiratunga N, Massie S, Cooper K. Personalised human activity recognition using matching networks. In International Conference on Case- based Reasoning 2018 Jul 9 (pp. 339-353). Springer, Cham. - Wijekoon, A., Wiratunga, N., Sani, S., Massie, S., & Cooper, K. (2018, July). Improving kNN for Human Activity Recognition with Privileged Learning Using Translation Models. In International Conference on Case-based Reasoning (pp. 448-463). Springer, Cham. - Wijekoon, A., Wiratunga, N., Sani, S., & Cooper, K. (2020). A knowledge-light approach to personalised and open-ended human activity recognition. Knowledge-based Systems, 192, 105651. Citation Request: Sani, S., Wiratunga, N., Massie, S., & Cooper, K. (2016, December). SELFBACK--activity recognition for self-management of low back pain. In?International Conference on Innovative Techniques and Applications of Artificial Intelligence?(pp. 281-294). Springer, Cham.

作者：Sadiq Sani、Nirmalie Wiratunga、Kay Cooper 罗伯特·戈登大学 阿伯丁，英国

数据集信息：SELFBACK数据集包含9类活动数据，其中6项为移动活动，3项为久坐活动，由33名受试者完成。数据由两台三轴加速度计（tri-axial accelerometers）采集，采样频率为100Hz，分别佩戴在受试者优势侧手腕与大腿位置。

**应用场景**：该数据集可用于开发人体活动识别相关算法，涵盖预处理、特征提取、传感器融合、数据分段与分类等研究方向。

**数据采集方法**：每名受试者完成每项活动的时长约为3分钟。

**传感器参数**：采用Axivity AX3三轴记录式加速度计，采样频率为100Hz，测量范围为±8g。

**活动类别**：
- 上楼行走
- 下楼行走
- 慢步行走
- 中速步行
- 快步行走
- 慢跑
- 站立
- 坐姿
- 躺卧

**数据文件夹结构**：SELFBACK数据集包含三个文件夹：两个分别对应单传感器模态，以"w"代表手腕传感器，"t"代表大腿传感器；另有一个融合双传感器模态的文件夹"wt"，通过时间戳将手腕与大腿传感器数据进行合并。在"w"与"t"文件夹内，各包含9个子文件夹，分别对应每一类活动；每个活动子文件夹内包含33个数据文件，对应每名受试者。在"wt"文件夹内，共包含297（33×9）个数据文件，文件名可指示对应受试者与活动类别。

**属性信息**：
"w"与"t"文件夹内的文件包含4列数据，格式如下：
1. 时间戳
2. X轴加速度值
3. Y轴加速度值
4. Z轴加速度值
数值范围为-8至+8。

"wt"文件夹内的文件包含6列数据，格式如下：
1. 手腕传感器X轴加速度值
2. 手腕传感器Y轴加速度值
3. 手腕传感器Z轴加速度值
4. 大腿传感器X轴加速度值
5. 大腿传感器Y轴加速度值
6. 大腿传感器Z轴加速度值
数值范围为-8至+8。

**相关研究论文**：
- Sani, S., Wiratunga, N., Massie, S., & Cooper, K. (2016年12月). SELFBACK——用于腰痛自我管理的活动识别方法. 载于：人工智能创新技术与应用国际会议（pp. 281-294）. Cham：Springer.
- Sani, S., Massie, S., Wiratunga, N., & Cooper, K. (2017年8月). 面向人体活动识别的深浅特征学习方法. 载于：知识科学、工程与管理国际会议（pp. 469-482）. Cham：Springer.
- Sani, S., Wiratunga, N., Massie, S., & Cooper, K. (2017年6月). 面向个性化人体活动识别的k近邻采样方法. 载于：基于案例推理国际会议（pp. 330-344）. Cham：Springer.
- Sani S, Wiratunga N, Massie S, Cooper K. 基于匹配网络的个性化人体活动识别方法. 载于：2018年基于案例推理国际会议（pp. 339-353）. Cham：Springer.
- Wijekoon, A., Wiratunga, N., Sani, S., Massie, S., & Cooper, K. (2018年7月). 基于特权学习与翻译模型的改进k近邻人体活动识别方法. 载于：基于案例推理国际会议（pp. 448-463）. Cham：Springer.
- Wijekoon, A., Wiratunga, N., Sani, S., & Cooper, K. (2020). 轻知识驱动的个性化开放式人体活动识别方法. 《基于知识的系统》, 192, 105651.

**引用要求**：Sani, S., Wiratunga, N., Massie, S., & Cooper, K. (2016年12月). SELFBACK——用于腰痛自我管理的活动识别方法. 载于：人工智能创新技术与应用国际会议（pp. 281-294）. Cham：Springer.

搜集汇总

背景与挑战

背景概述

该数据集名为SELFBACK，专注于人类活动识别，包含9个活动类别，其中6个为步行活动（如上楼、下楼、不同速度步行和慢跑），3个为静坐活动（站立、坐着和躺着）。数据由33名参与者通过佩戴在手腕和大腿上的三轴加速度计采集，采样率为100Hz，适用于算法开发如预处理、特征提取和分类任务。