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Classification of Signals

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Related Experiment Video

Updated: Nov 15, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

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Published on: March 28, 2025

A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic

Mohammed Asfour¹, Carlo Menon^2,3, Xianta Jiang¹

¹Department of Computer Science, Memorial University of Newfoundland, St. John's, NL A1B 3X5, Canada.

Sensors (Basel, Switzerland)

|March 6, 2021

Summary

This summary is machine-generated.

Force Myography (FMG) offers a new way for hand gesture recognition. A novel signal processing pipeline using manifold learning improves FMG signal robustness, boosting classifier accuracy by 5%.

Keywords:

data pre-processing force myography hand gestures recognition machine learning

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Area of Science:

Biomedical Engineering
Machine Learning
Signal Processing

Background:

Surface ElectroMyography (sEMG) is a common method for hand gesture recognition.
Force Myography (FMG) is an emerging alternative for hand gesture recognition.
Current research focuses on machine learning algorithms and feature engineering for FMG.

Purpose of the Study:

To introduce a novel signal processing pipeline for Force Myography (FMG).
To enhance hand gesture recognition performance using manifold learning for robust signal representation.
To evaluate the pipeline's effectiveness compared to raw FMG signals.

Main Methods:

A novel signal processing pipeline utilizing manifold learning was developed.
The pipeline was applied to an FMG dataset from nine participants across three sessions.
Various classification algorithms were used to assess performance against raw FMG signals.

Main Results:

The proposed pipeline reduced within-gesture variance and maximized between-gesture variance.
This led to improved robustness and temporal consistency in hand gesture classification.
Classification accuracy improved by an average of 5% across different classifiers.

Conclusions:

The manifold learning-based signal processing pipeline significantly enhances FMG-based hand gesture recognition.
The method offers improved robustness and accuracy, outperforming raw FMG signal analysis.
This approach represents a promising advancement for reliable human-computer interaction using FMG.