Multivariate Gait Data
Donated on 12/14/2022
Bilateral (left, right) joint angle (ankle, knee, hip) times series data collected from 10 healthy subjects under 3 walking conditions (unbraced, knee braced, ankle braced). For each condition, each subject’s data consists of 10 consecutive gait cycles.
Dataset Characteristics
Sequential, Multivariate, Time-Series
Subject Area
Health and Medicine
Associated Tasks
Classification, Regression, Clustering
Feature Type
Real, Categorical, Integer
# Instances
181800
# Features
7
Dataset Information
For what purpose was the dataset created?
Biomechanical analysis of human locomotion
Who funded the creation of the dataset?
National Science Foundation (#0540834) and Mary Jane Neer Disability Research Fund at the University of Illinois
Additional Information
This dataset is a six dimensional array of joint angle data: 10 subjects x 3 conditions x 10 replications x 2 legs x 3 joints x 101 time points. The data were recored from ten subjects under three different conditions: normal (unbraced) walking on a treadmill, walking on a treadmill with a knee-brace on the right knee, and walking on a treadmill with an ankle brace on the right ankle. For each subject in each condition, ten consecutive gait cycles (replications) are included, where each gait cycle starts and ends at heel-strike. For each gait cycle, the data were normalized to consist of 101 time points representing 0%,…,100% of the gait cycle. Six joint angles are included, which comprise all combinations of leg (left and right) and joint (ankle, knee, hip). The data were collected at the Human Dynamics and Controls Laboratory at the University of Illinois at Urbana-Champaign. Details of the experimental setup can be found in Shorter et al. (2008). Details on the data preprocessing can be found in Helwig et al. (2011). The data were published as supplementary materials by Helwig et al. (2016). Attribute Information: 1. subject: 1 = subject 1, …, 10 = subject 10 (integer) 2. condition: 1 = unbraced, 2 = knee brace, 3 = ankle brace (integer) 3. replication: 1 = replication 1, …, 10 = replication 10 (integer) 4. leg: 1 = left, 2 = right (integer) 5. joint: 1 = ankle, 2 = knee, 3 = hip (integer) 6. time: 0 = 0% gait cycle, …, 100 = 100% gait cycle (integer) 7. angle: joint angle in degrees (real valued)
Has Missing Values?
No
Introductory Paper
By Nathaniel E. Helwig, K. A. Shorter, Ping Ma, E. Hsiao-Wecksler. 2016
Published in Journal of Biomechanics
Variables Table
Variable Name | Role | Type | Description | Units | Missing Values |
---|---|---|---|---|---|
subject | Feature | Categorical | 1 = subject 1, …, 10 = subject 10 | no | |
condition | Feature | Categorical | 1 = unbraced, 2 = knee brace, 3 = ankle brace | no | |
replication | Feature | Integer | 1 = replication 1, …, 10 = replication 10 | no | |
leg | Feature | Categorical | 1 = left, 2 = right | no | |
joint | Feature | Categorical | 1 = ankle, 2 = knee, 3 = hip | no | |
time | Feature | Integer | 0 = 0% gait cycle, …, 100 = 100% gait cycle | no | |
angle | Feature | Continuous | joint angle in degrees | no |
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Dataset Files
File | Size |
---|---|
gait.csv | 5.2 MB |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset multivariate_gait_data = fetch_ucirepo(id=760) # data (as pandas dataframes) X = multivariate_gait_data.data.features y = multivariate_gait_data.data.targets # metadata print(multivariate_gait_data.metadata) # variable information print(multivariate_gait_data.variables)
Helwig, N. & Hsiao-Wecksler, E. (2016). Multivariate Gait Data [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5861T.
Keywords
Creators
Nathaniel Helwig
helwig@umn.edu
University of Minnesota
Elizabeth Hsiao-Wecksler
ethw@illinois.edu
University of Illinois at Urbana-Champaign
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.