selfBACK
Donated on 6/14/2020
The SELFBACK dataset is a Human Activity Recognition Dataset of 9 activity classes recorded with two tri-axial accelerometers.
Dataset Characteristics
Time-Series
Subject Area
Computer Science
Associated Tasks
Classification, Clustering
Feature Type
Real
# Instances
26136
# Features
6
Dataset Information
Additional 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.
Has Missing Values?
No
Variable 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
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset selfback = fetch_ucirepo(id=521) # data (as pandas dataframes) X = selfback.data.features y = selfback.data.targets # metadata print(selfback.metadata) # variable information print(selfback.variables)
selfBACK. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C5SK65.
@misc{misc_selfback_521,
title = {{selfBACK}},
year = {2020},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5SK65}
}DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.