Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.6K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.4K
Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Survival Tree01:19

Survival Tree

50
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
50
Aggregates Classification01:29

Aggregates Classification

298
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
298
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.7K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Advances in Wearable Bioimaging.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Transformer-Based Context-Informed Incremental Learning With sDTW Alignment Unlocks Fast and Precise Regression-Based Myoelectric Control.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Context Informed Incremental Learning Improves Myoelectric Control Performance in Virtual Reality Object Manipulation Tasks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Towards Next-Generation Myoelectric Prostheses: 3D-Printed Electrode Arrays for Gesture Recognition.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Investigating Feedback-Informed Screen-Guided Training to Enhance Myoelectric Control and Predictability.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same author

From zero- to few-shot: deep temporal learning of wrist EMG enables scalable cross-user gesture recognition.

Journal of neural engineering·2025

Related Experiment Video

Updated: May 24, 2025

Spotting Cheetahs: Identifying Individuals by Their Footprints
09:47

Spotting Cheetahs: Identifying Individuals by Their Footprints

Published on: May 1, 2016

14.7K

Covariate Analysis for Footstep Recognition Using Unsupervised Hierarchical Clustering.

Neha Kulkarni, Robyn Larracy, Angkoon Phinyomark

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary

    Footstep recognition biometric systems are influenced by covariates like age and weight. Hierarchical clustering revealed distinct gait patterns, suggesting these factors can improve or bias recognition systems.

    More Related Videos

    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    13.5K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.9K

    Related Experiment Videos

    Last Updated: May 24, 2025

    Spotting Cheetahs: Identifying Individuals by Their Footprints
    09:47

    Spotting Cheetahs: Identifying Individuals by Their Footprints

    Published on: May 1, 2016

    14.7K
    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    13.5K
    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.9K

    Area of Science:

    • Biometrics
    • Human-Computer Interaction
    • Gait Analysis

    Background:

    • Footstep recognition is an emerging biometric technology.
    • The influence of covariates on footstep recordings is not well understood compared to established biometrics.
    • Understanding these influences is crucial for developing robust footstep recognition systems.

    Purpose of the Study:

    • To investigate the impact of internal and external covariates on spatial and temporal footstep features.
    • To apply unsupervised hierarchical clustering (HCA) to identify distinct gait patterns based on these features.
    • To determine how factors like body weight, age, race, sex, shoe type, and walking speed affect footstep data.

    Main Methods:

    • Utilized unsupervised hierarchical clustering (HCA) on footstep pressure patterns from twenty individuals.
    • Analyzed spatial representations (peak pressure images) and temporal representations (ground reaction force and center of pressure time series).
    • Employed 22 cluster validity indices to ensure a robust clustering technique.

    Main Results:

    • Identified two distinct clusters in both spatial and temporal gait representations using HCA.
    • Found that body weight, age, race, and shoe type were distinguishable factors for these clusters.
    • Observed trends related to sex and walking speed exclusively within the temporal domain of gait patterns.

    Conclusions:

    • Covariates significantly influence footstep biometric data, creating distinct gait clusters.
    • Footstep biometric systems can potentially use covariate information as soft biometrics for enhanced recognition.
    • Mitigation strategies may be necessary to address model bias and improve generalization in footstep recognition systems.