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

You might also read

Related Articles

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

Sort by
Same author

Estimating the valence and arousal of dyadic conversations using autonomic nervous system responses and regression algorithms.

Frontiers in neuroergonomics·2025
Same author

TruVox Web-Based Software for Vocal Pitch Training in Transgender Women: Development and Single-Session Evaluations.

JMIR formative research·2025
Same author

Cardiovascular Assessment of Manual Wheelchair Users with 6-Minute Push Test: VO<sub>2</sub> Formula.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same author

Assessing the Impact of Loading on Mobility and Physical Exertion of Manual Wheelchair Users Using Wearable Sensors.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same author

What If You Cannot See and Do Not Know? The Effects of Vision and Knowledge of Landing Heights on Single-Leg Prelanding and Early Landing Mechanics.

Journal of applied biomechanics·2025
Same author

A Practical Cardiovascular Health Assessment for Manual Wheelchair Users During the 6-Minute Push Test.

Sensors (Basel, Switzerland)·2025

Related Experiment Video

Updated: Dec 10, 2025

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.1K

Load Position and Weight Classification during Carrying Gait Using Wearable Inertial and Electromyographic Sensors.

Maja Goršič1,2, Boyi Dai2, Domen Novak1

  • 1Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY 82071, USA.

Sensors (Basel, Switzerland)
|September 5, 2020
PubMed
Summary

Wearable sensors accurately classify load position and weight during walking. This technology could enhance worker monitoring and control assistive devices in physically demanding jobs.

Keywords:
carryingclassificationelectromyographygaitinertial measurement unitssupervised machine learningwearable sensors

More Related Videos

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.0K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.0K

Related Experiment Videos

Last Updated: Dec 10, 2025

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.1K
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

9.0K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.0K

Area of Science:

  • Biomechanics
  • Human-Computer Interaction
  • Wearable Technology

Background:

  • Physically demanding jobs involve lifting and carrying heavy loads.
  • Previous research used wearable sensors for object pickup classification.
  • Limited use of sensors for classifying load position and weight during gait.

Purpose of the Study:

  • To investigate the use of wearable sensors for classifying load position and weight during gait.
  • To develop and evaluate machine learning algorithms for this classification task.

Main Methods:

  • Utilized wearable inertial and electromyography (EMG) sensors.
  • Ten participants performed 19 carrying trials each with different load positions and weights.
  • Trained and evaluated supervised machine learning algorithms for classification.

Main Results:

  • 100% accuracy in differentiating frontal loads from side/none.
  • 96.1% accuracy in distinguishing frontal, unilateral, and bilateral side loads.
  • 75-79% accuracy in classifying load asymmetry levels.

Conclusions:

  • Wearable sensors can accurately differentiate load positions and weights during gait.
  • Future applications include assistive device control and long-term worker monitoring.
  • Further research needed with arm EMG and diverse load conditions.