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

Classifying Matter by Composition03:35

Classifying Matter by Composition

90.6K
Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
90.6K
¹H NMR Signal Multiplicity: Splitting Patterns01:13

¹H NMR Signal Multiplicity: Splitting Patterns

6.9K
When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...
6.9K
Classifying Matter by State02:49

Classifying Matter by State

103.8K
Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
103.8K
Muscles of the Leg that Move the Foot and Toes01:28

Muscles of the Leg that Move the Foot and Toes

4.1K
The human leg comprises an intricate system of muscles that facilitate the movement of feet and toes. Within this system, the muscles are categorized into the anterior, lateral, and posterior compartments, each with a unique set of muscles carrying out specific functions.
Anterior Compartment
The anterior compartment includes muscles that contribute to the dorsiflexion of the foot. This compartment houses the tibialis anterior, extensor hallucis longus, and extensor digitorum longus muscles....
4.1K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

554
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
554
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

963
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
963

You might also read

Related Articles

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

Sort by
Same author

Emotion Perception in Behavioral Variant Frontotemporal Dementia and Alzheimer Disease: The Parahippocampal Conundrum.

Cognitive and behavioral neurology : official journal of the Society for Behavioral and Cognitive Neurology·2026
Same author

Ankle Joint Biomechanics in Recreational Runners with Resolved and Incident Plantar Fasciitis: A One-Year Prospective 4HAIE Cohort Study.

Scandinavian journal of medicine & science in sports·2026
Same author

Relationship Between Decomposition of Surface Electromyography Signals and Force Production: Analyzing Recovery From Intense Exercise.

Journal of applied biomechanics·2026
Same author

Biomechanical insights into Achilles tendinopathy risk and protection in runners: a large prospective study 4HAIE.

British journal of sports medicine·2026
Same author

The influence of sex on shoulder and hip joint resting position and mobility in elite golfers.

Scientific reports·2026
Same author

Integration of coordination and kinetic analysis reveals mechanisms of upper limb joint loading and technique-specific strategies in female gymnastics.

Sports biomechanics·2026

Related Experiment Video

Updated: Feb 5, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

Using a Support-Vector Machine Algorithm to Classify Lower Extremity EMG Signals During Running Shod/Unshod With

Ricardo Pires1, Thays Falcari1, Alexandre B Campo1

  • 11 Laboratório de Controle Aplicado, Instituto Federal de Educação, Ciência e Tecnologia de São Paulo, São Paulo, SP, BR.

Journal of Applied Biomechanics
|September 13, 2018
PubMed
Summary

Fast Fourier Transformation (FFT) effectively classified foot strike patterns using electromyography (EMG) signals. Support Vector Machines (SVM) trained with FFT data showed higher accuracy than Discrete Wavelet Transformation (DWT) for differentiating rearfoot and forefoot running.

Keywords:
emg classificationforefoot runningrearfoot runningrunning shoes

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K

Related Experiment Videos

Last Updated: Feb 5, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.5K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K

Area of Science:

  • Biomechanics
  • Sports Science
  • Machine Learning in Sports

Background:

  • Understanding running mechanics is crucial for injury prevention and performance enhancement.
  • Differentiating between rearfoot and forefoot running patterns can provide insights into biomechanical efficiency.
  • Electromyography (EMG) signals offer valuable data for analyzing muscle activity during locomotion.

Purpose of the Study:

  • To employ a Support Vector Machine (SVM) algorithm for classifying shod vs. barefoot running and rearfoot vs. forefoot landings.
  • To evaluate the efficacy of Discrete Wavelet Transformation (DWT) and Fast Fourier Transformation (FFT) in processing EMG signals for SVM classification.

Main Methods:

  • Recorded thigh and leg muscle surface electromyography (EMG) from ten habitually shod runners.
  • Utilized Discrete Wavelet Transformation (DWT) and Fast Fourier Transformation (FFT) for feature extraction from EMG signals.
  • Trained a Support Vector Machine (SVM) classifier using the extracted features to differentiate running styles and landing patterns.

Main Results:

  • Fast Fourier Transformation (FFT) coefficients from gastrocnemius and tibialis anterior muscles yielded the highest accuracy (76% and 67%) in differentiating rearfoot/forefoot landings.
  • The SVM classification using FFT showed a low probability of random chance (0.5% and 4%).
  • Discrete Wavelet Transformation (DWT) resulted in lower classification accuracy (60% and 53%) with higher probabilities of random outcomes (15% and 35%).
  • Shod vs. barefoot running could not be reliably differentiated using the applied methods.

Conclusions:

  • Fast Fourier Transformation (FFT) is a more effective signal processing technique than Discrete Wavelet Transformation (DWT) for training Support Vector Machines (SVM) to classify rearfoot vs. forefoot running patterns based on EMG data.
  • The study highlights the potential of machine learning algorithms combined with specific signal processing techniques for analyzing running biomechanics.
  • Further research is needed to improve the classification of shod vs. barefoot running.