Person Classification Gait Data
Donated on 3/1/2020
Gait is considered a biometric criterion. Therefore, we tried to classify people with gait analysis with this gait data set.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Real
# Instances
48
# Features
321
Dataset Information
Additional Information
This data set was created by calculating the walking parameters of a total of 16 different volunteers, 7 female and 9 male. The volunteers of 16 volunteers ranged between 20 and 34 years old, and their weight ranged from 53 to 95. In order to calculate each walking parameter, people were asked to walk the 30-meter long course for three rounds. The shared file contains X and Y data. X represents gait data and y represents person information for the relevant sample.
Has Missing Values?
No
Variable Information
Gait Data consists of the following parameters. Basic Parameters (Speed, Variability, Symmetry), Temporary Parameters (Heel Press Time, Cycle Time, Cadence, Posture, Oscillation, Loading, Foot Press, Thrust, Double Support), Spatial Parameters (Step Length, Step Speed, Peak Angle Speed, Maximum Swing Speed, Rotation Angle, Step Angle, Lift Angle, Swing Width, 3D Path Length), Height Parameters (Maximum Heel Height, Maximum Finger Tip Height, Minimum Finger Tip Height)
Dataset Files
| File | Size |
|---|---|
| PersonGaitDataSet.mat | 42.4 KB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset person_classification_gait_data = fetch_ucirepo(id=561) # data (as pandas dataframes) X = person_classification_gait_data.data.features y = person_classification_gait_data.data.targets # metadata print(person_classification_gait_data.metadata) # variable information print(person_classification_gait_data.variables)
Person Classification Gait Data [Dataset]. (2020). UCI Machine Learning Repository. https://doi.org/10.24432/C5PP68.
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.