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

Distance Problem01:29

Distance Problem

180
When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
180
Distance Measurements by Taping01:18

Distance Measurements by Taping

628
Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
628
Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

670
Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over...
670
Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

55.7K
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
55.7K
Measurement: Derived Units03:02

Measurement: Derived Units

57.6K
The International System of Units or SI system, by international agreement, has fixed measurement units for seven fundamental properties: length, mass, time, temperature, electric current, amount of substance, and luminosity. These are called the SI base units.
57.6K
Inertial Frames of Reference01:03

Inertial Frames of Reference

9.9K
Newton’s first law is usually considered to be a statement about reference frames. It provides a method for identifying a special type of reference frame: the inertial reference frame. In principle, we can make the net force on a body zero. If its velocity relative to a given frame is constant, then that frame is said to be inertial. So, by definition, an inertial reference frame is a reference frame where Newton's first law holds valid. Newton's first law applies to objects with...
9.9K

You might also read

Related Articles

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

Sort by
Same author

The highs and lows of lifting loads: SPM analysis of multi-segmental spine angles in healthy adults during manual handling with increased load.

Frontiers in bioengineering and biotechnology·2024
Same author

A Green Synthesis Route to Derive Carbon Quantum Dots for Bioimaging Cancer Cells.

Nanomaterials (Basel, Switzerland)·2023
Same author

Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review.

Sensors (Basel, Switzerland)·2022
Same author

Carbon Dot Therapeutic Platforms: Administration, Distribution, Metabolism, Excretion, Toxicity, and Therapeutic Potential.

Small (Weinheim an der Bergstrasse, Germany)·2022
Same author

Prediction of gait trajectories based on the Long Short Term Memory neural networks.

PloS one·2021
Same author

A cross-sectional study of foot-ground clearance in healthy community dwelling Japanese cohorts aged 50, 60 and 70 years.

BMC geriatrics·2021

Related Experiment Video

Updated: Mar 30, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

8.8K

A machine learning approach to estimate Minimum Toe Clearance using Inertial Measurement Units.

Braveena K Santhiranayagam1, Daniel T H Lai2, W A Sparrow1

  • 1Institute of Sport, Exercise and Active Living, Victoria University, Melbourne, Victoria 8001, Australia; College of Sport & Exercise Science, Victoria University, Melbourne, Victoria 8001, Australia.

Journal of Biomechanics
|November 18, 2015
PubMed
Summary

This study introduces a machine learning method using wearable sensors to accurately estimate minimum toe clearance (MTC) height, a key factor in preventing falls in older adults.

Keywords:
GaitGeneralized Regression Neural Network (GRNN)Inertial Measurement Unit (IMU)Machine learningMinimum Toe Clearance (MTC)

More Related Videos

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
08:08

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Published on: May 8, 2014

17.4K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.4K

Related Experiment Videos

Last Updated: Mar 30, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

8.8K
Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
08:08

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Published on: May 8, 2014

17.4K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.4K

Area of Science:

  • Biomechanics
  • Wearable Technology
  • Machine Learning

Background:

  • Falls are a leading cause of injury and death in older adults, with tripping during walking being a major contributor.
  • Minimum toe clearance (MTC) is a critical gait parameter for assessing tripping risk.
  • Wearable Inertial Measurement Units (IMUs) offer a practical solution for gait monitoring, but noise limits accuracy in estimating MTC height.

Purpose of the Study:

  • To develop and validate a machine learning approach for accurate MTC height estimation using IMU data.
  • To investigate the effectiveness of Generalized Regression Neural Networks (GRNN) with selected features for MTC height prediction.
  • To assess the potential for real-time, out-of-laboratory gait monitoring for fall prevention.

Main Methods:

  • Utilized Inertial Measurement Units (IMUs) containing accelerometers and gyroscopes to collect gait data.
  • Employed a machine learning approach, specifically Generalized Regression Neural Networks (GRNN), trained on raw and integrated inertial signals.
  • Implemented a hill-climbing feature-selection method to identify optimal features for MTC height estimation.

Main Results:

  • The GRNN model achieved a root-mean-square-error (RMSE) of 6.6mm for young adults and 7.1mm for older adults.
  • The developed method demonstrated approximately 68% less RMSE compared to existing MTC height estimation techniques.
  • The study identified optimal feature sets (9 for young adults, 5 for older adults) for accurate prediction.

Conclusions:

  • The proposed GRNN-based MTC height estimation method shows high accuracy and significant improvement over previous techniques.
  • This approach holds strong potential for real-time gait monitoring during everyday activities, aiding in the prevention of falls in older adults.
  • Practical, wearable gait analysis using IMUs can provide valuable insights into MTC height for enhanced fall risk assessment.