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

Smoothing spline analysis of variance models: A new tool for the analysis of cyclic biomechanical data.

By Nathaniel E. Helwig, K. A. Shorter, Ping Ma, E. Hsiao-Wecksler. 2016

Published in Journal of Biomechanics

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
subjectFeatureCategorical1 = subject 1, …, 10 = subject 10no
conditionFeatureCategorical1 = unbraced, 2 = knee brace, 3 = ankle braceno
replicationFeatureInteger1 = replication 1, …, 10 = replication 10no
legFeatureCategorical1 = left, 2 = rightno
jointFeatureCategorical1 = ankle, 2 = knee, 3 = hipno
timeFeatureInteger0 = 0% gait cycle, …, 100 = 100% gait cycleno
angleFeatureContinuousjoint angle in degreesno

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Keywords

hierarchical time seriesMulti-class classification Multivariate regressionsensor datatime serieswearable sensing

Creators

Nathaniel Helwig

helwig@umn.edu

University of Minnesota

Elizabeth Hsiao-Wecksler

ethw@illinois.edu

University of Illinois at Urbana-Champaign

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