Supervised Machine Learning based Fast Hand Gesture Recognition and Classification Using Electromyography (EMG) Signals

Khan, Misha Urooj; Khan, Hareem; Muneeb, Muhammad; Abbasi, Zeeshan; Abbasi, Usman Babar; Baloch, Naveed Khan

doi:10.1109/icaem53552.2021.9547148

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…Precision (88 ± 6%, in Figure 10 a) and sensitivity (88 ± 6%, Figure 10 b) were lower than previously reported values, i.e., 98% [ 43 , 44 ]. Considering the function-driven nature of a prosthetic hand, precision is usually considered more important than sensitivity for classification models [ 45 ].…”

Section: Discussioncontrasting

confidence: 71%

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes

Shaw,

Devin,

Tang

et al. 2024

Sensors

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Classification-based myoelectric control has attracted significant interest in recent years, leading to prosthetic hands with advanced functionality, such as multi-grip hands. Thus far, high classification accuracies have been achieved by increasing the number of surface electromyography (sEMG) electrodes or adding other sensing mechanisms. While many prescribed myoelectric hands still adopt two-electrode sEMG systems, detailed studies on signal processing and classification performance are still lacking. In this study, nine able-bodied participants were recruited to perform six typical hand actions, from which sEMG signals from two electrodes were acquired using a Delsys Trigno Research+ acquisition system. Signal processing and machine learning algorithms, specifically, linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines (SVM), were used to study classification accuracies. Overall classification accuracy of 93 ± 2%, action-specific accuracy of 97 ± 2%, and F1-score of 87 ± 7% were achieved, which are comparable with those reported from multi-electrode systems. The highest accuracies were achieved using SVM algorithm compared to LDA and KNN algorithms. A logarithmic relationship between classification accuracy and number of features was revealed, which plateaued at five features. These comprehensive findings may potentially contribute to signal processing and machine learning strategies for commonly prescribed myoelectric hand systems with two sEMG electrodes to further improve functionality.

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Section: Discussioncontrasting

confidence: 71%

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes

Shaw,

Devin,

Tang

et al. 2024

Sensors

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“…The use of deep learning in EMG based hand gesture recognition has shown improvement in the classification accuracy [77,78]. Use of the spectral feature domains for building ML hand gesture recognition system might assist physically challenged persons with non-invasive machine communication and disabled people with nonverbal communication [79]. Because ML algorithms may adjust to individual differences, the system is robust and individualized.…”

Section: Gesture Recognitionmentioning

confidence: 99%

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

Ojha

2023

Extsv. Rev.

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Electromyography (EMG) is about studying electrical signals from muscles and can provide a wealth of information on the function, contraction, and activity of your muscles. In the field of EMG pattern recognition, these signals are used to identify and categorize patterns linked to muscle activity. Various machine learning (ML) methods are used for this purpose. Successful detection of these patterns depends on using effective signal-processing techniques. It is crucial to reduce noise in EMG for accurate and meaningful information about muscle activity, improving signal quality for precise assessments. ML tools such as SVMs, neural networks, KNNs, and decision trees play a crucial role in sorting out complex EMG signals for different pattern recognition tasks. Clustering algorithms also help analyze and interpret muscle activity. EMG and ML find diverse uses in rehabilitation, prosthetics, and human-computer interfaces, though real-time applications come with challenges. They bring significant changes to prosthetic control, human-computer interfaces, and rehabilitation, playing a vital role in pattern recognition. They make prosthetic control more intuitive by understanding user intent from muscle signals, enhance human-computer interaction with responsive interfaces, and support personalized rehabilitation for those with motor impairments. The combination of EMG and ML opens doors for further research into understanding muscle behavior, improving feature extraction, and advancing classification algorithms.

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“…Artificial intelligence (AI) systems are used to do complex tasks in a way similar to how humans solve problems [16,17,18,19]. Machine learning is a branch of AI, which is usually characterized as a machine's ability to replicate intelligent human behavior [20,21,22,23,24].…”

Section: Machine Learning Based Fall Detectionmentioning

confidence: 99%

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Ishtiaq

Saeed

Khan

et al. 2022

JAREE

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Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.

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Supervised Machine Learning based Fast Hand Gesture Recognition and Classification Using Electromyography (EMG) Signals

Cited by 10 publications

References 12 publications

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes

An Introduction to Electromyography Signal Processing and Machine Learning for Pattern Recognition: A Brief Overview

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

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