Related Experiment Video
Updated: Feb 5, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
Published on: January 5, 2024
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.
Abstract:
The present study aimed to use a Support Vector Machine (SVM) algorithm to identify and classify shod and barefoot running as well as rearfoot and forefoot landings. Ten habitually shod runners ran at self-selected speed. Thigh and leg muscle surface electromyography (EMG) were recorded. Discrete Wavelet transformation (DWT) and Fast Fourier transformation (FFT) were used for the assembly of vectors for training and classification of a SVM. Using the FFT coefficients for the gastrocnemius and tibialis anterior muscles presented the best results for differentiating between rearfoot/forefoot running in the window before foot-floor contact possibly due to these muscles' critical role in determining which part of the foot will first touch the floor. The classification rate was 76% and 67% respectively, with a probability of being random of 0.5% and 4% respectively. For the same terms and conditions of classification, the DWT produced a reduction in the percentage of correctness of 60% and 53% with a probability of having reached these levels randomly of 15% and 35%. In conclusion, based on EMG signals, the use a FFT to train a SVM was a better option to differentiate running forefoot/rearfoot than to use the DWT. Shod/barefoot running could not be differentiated.
Related Concept Videos
Classifying Matter by Composition
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...
¹H NMR Signal Multiplicity: Splitting Patterns
Classifying Matter by State
Muscles of the Leg that Move the Foot and Toes
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....
Wald-Wolfowitz Runs Test II
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
Wald-Wolfowitz Runs Test I
The test works...

