Performance assessment of machine learning algorithms and ensemble techniques for hand gesture recognition using electromyographic signals

S Dhumal, P Sharma - 2023 IEEE International Conference on …, 2023 - ieeexplore.ieee.org
The measuring of electrical activity produced by muscular contractions is the objective of
electromyography (EMG). In contrast, hand gesture recognition focuses on recognizing a …

Electromyography Gesture Model Classifier for Fault-Tolerant-Embedded Devices by Means of Partial Least Square Class Modelling Error Correcting Output Codes …

P Sarabia, A Araujo, LA Sarabia, MC Ortiz - Algorithms, 2023 - mdpi.com
Surface electromyography (sEMG) plays a crucial role in several applications, such as for
prosthetic controls, human–machine interfaces (HMI), rehabilitation, and disease diagnosis …

Classification Algorithms Trained on Simple (Symmetric) Lifting Data Perform Poorly in Predicting Hand Loads during Complex (Free-Dynamic) Lifting Tasks

S Taori, S Lim - Available at SSRN 4878030, 2024 - papers.ssrn.com
The performance of machine learning (ML) algorithms is dependent on which dataset it has
been trained on. While ML algorithms are increasingly used for lift risk assessment, many …

sEMG biceps and triceps effort signals classification using 1D-CNN convolution

ST Kebir, F Berrhail - Studies in Engineering and …, 2024 - ojs.studiespublicacoes.com.br
In this paper, we present a system for acquiring and classifying surface physiological
muscles signals (sEMG) for the biceps and triceps muscles during movement or work, as …

[PDF][PDF] Armband EMG-based Lifting Detection and Load Classification Algorithms using Static and Dynamic Lifting Trials

SP Taori - 2023 - vtechworks.lib.vt.edu
The high prevalence of work-related musculoskeletal disorders in occupational settings
necessitates the development of economic, accurate, and convenient methods for …