Accelerometer Gyro Mobile Phone
Donated on 11/24/2022
data collected on 2022, in King Saud University in riyadh for recognizing human activities using mobile phone IMU sensors (Accelerometer, and Gyroscope). these activity is calssified to standing(stop), or walking.
Dataset Characteristics
Tabular, Sequential, Multivariate, Time-Series
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Real, Categorical
# Instances
31991
# Features
8
Dataset Information
Has Missing Values?
No
Introductory Paper
By A. Alsahly, M. Hassan, K. Saleem, Amerah Alabrah, J. Rodrigues. 2022
Published in Italian National Conference on Sensors
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| accX | Feature | Continuous | no | ||
| accY | Feature | Continuous | no | ||
| accZ | Feature | Continuous | no | ||
| gyroX | Feature | Continuous | no | ||
| gyroY | Feature | Continuous | no | ||
| gyroZ | Feature | Continuous | no | ||
| timestamp | Feature | Categorical | no | ||
| Activity | Feature | Integer | no |
0 to 8 of 8
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset accelerometer_gyro_mobile_phone = fetch_ucirepo(id=755) # data (as pandas dataframes) X = accelerometer_gyro_mobile_phone.data.features y = accelerometer_gyro_mobile_phone.data.targets # metadata print(accelerometer_gyro_mobile_phone.metadata) # variable information print(accelerometer_gyro_mobile_phone.variables)
AlSahly,Abdullah. (2022). Accelerometer Gyro Mobile Phone. UCI Machine Learning Repository. https://doi.org/10.3390/s22176513.
@misc{misc_accelerometer_gyro_mobile_phone_755,
author = {AlSahly,Abdullah},
title = {{Accelerometer Gyro Mobile Phone}},
year = {2022},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.3390/s22176513}
}Keywords
Creators
Abdullah AlSahly
getsmarter01@gmail.com
KSU
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.