EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine
Reza Bagherian Azhiri, Mohammad Esmaeili, Mohsen Jafarzadeh, Mehrdad Nourani
Abstract
Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with high accuracy is available. In this paper, we propose to utilize Extreme Value Machine (EVM) as a high-performance algorithm for the classification of EMG signals. We employ reflection coefficients obtained from an Autoregressive (AR) model to train a set of classifiers. Our experimental results indicate that EVM has better accuracy in comparison to the conventional classifiers approved in the literature based on K-Nearest Neighbors (KNN) and Support Vector Machine (SVM).
Related Papers
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation2025-09-04Adversarial attacks to image classification systems using evolutionary algorithms2025-07-17Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation2025-07-16Safeguarding Federated Learning-based Road Condition Classification2025-07-16AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)2025-07-13Visual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions2025-07-06Fuzzy Classification Aggregation for a Continuum of Agents2025-07-06Hybrid-View Attention for csPCa Classification in TRUS2025-07-04