Hand Gesture Recognition Based on Surface Electromyogram Signal (sEMG) with Muscular Contraction Level and Real Time Implementation on An Artificial Prosthetic Wrist Using Artificial Neural Network (ANN)

Hasan, Ahmed Faiyaz; Masud, Hozaif Ul; Anzar, Tansif; Hasan, Probudha

doi:10.1109/tensymp50017.2020.9231038

Cited by 3 publications

(7 citation statements)

References 14 publications

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“…N is the sample data length and Xn is the value for each data a. Mean Absolute Value (MAV) [34]: In most cases, the rectified moving average and the mean absolute value are very similar to one another. An equation (Eq.…”

Section: G Feature Extractionmentioning

confidence: 99%

Machine Learning Technique to Classify EMG Signal for Diabetes Person

Zulkifli,

Nasir

2023

ijmst

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Diabetes can cause a disease known as diabetic peripheral neuropathy (DPN), which affects the blood vessels and nerves in the legs and feet. This condition can result in plantar foot ulcers and muscular weakness. Detecting DPN early stage is crucial so patients can receive early treatment before their disease worsens. Most technology that detects this disease is usually expensive, like an Electromyography machine (EMG). But, with the increasing popularity of machine learning classification in the health sciences, DPN can be identified early by producing a low-cost equipment. This study aimed to develop a low-cost surface EMG (sEMG) system to detect electrical activity in the lower limb muscles and classify healthy and diabetic subjects during muscle fatigue using K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN) as two methods of machine learning technique. This study used Muscle Sensor V3 as sEMG to record the signal and extract using time domain feature extraction before classification. In KNN, 1–10 values (K) are used, while in ANN, 1–10 values of the number of hidden neurons are used to compare the classification performance. The result shows that ANN is suitable compared to KNN with the method used in this study. ANN algorithm performs better using four hidden neurons with an accuracy of 100% for the training and testing process. The study can conclude that this low-cost sEMG system, with the help of ANN, can effectively classify two subjects (healthy and diabetic) according to the EMG data obtained. This system can help to identify any diabetes neuropathy at an early stage and able to prevent lower limb complications related to the disease.

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Section: G Feature Extractionmentioning

confidence: 99%

Machine Learning Technique to Classify EMG Signal for Diabetes Person

Zulkifli,

Nasir

2023

ijmst

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“…Mean Absolute Value (MAV) [34]: The mean absolute value and the moving average rectified value (ARV) are probably similar. Expression is given below.…”

Section: F Feature Extractionmentioning

confidence: 99%

“…Root Mean Square (RMS) [34]: This is a measurement of the square root of the input signal, expressed as:…”

Section: F Feature Extractionmentioning

confidence: 99%

“…Variance (VAR) [34]: If the variable signal is squared and then divided by the mean value, then the variance is identified, which is stated as:…”

Section: F Feature Extractionmentioning

confidence: 99%

“…Standard Deviation (SD) [34]: Using the following expression, the threshold level of muscular contraction activity is determined:…”

Section: F Feature Extractionmentioning

confidence: 99%

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Classification of Electromyography Signal of Diabetes using Artificial Neural Networks

Zulkifli¹,

Nasir²

2022

IJACSA

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Diabetes is one of the most chronic diseases, with an increasing number of sufferers yearly. It can lead to several serious complications, including diabetic peripheral neuropathy (DPN). DPN must be recognized early to receive appropriate treatment and prevent disease exacerbation. However, due to the rapid development of machine learning classification, like in the health science sector, it is very easy to identify DPN in the early stages. Therefore, the aim of this study is to develop a new method for detecting neuropathy based on the myoelectric signal among diabetes patients at a low cost with utilizing one of the machine learning techniques, the artificial neural network (ANN). To that aim, muscle sensor V3 is used to record the activity of the anterior tibialis muscle. Then, the representative time domain features which is mean absolute value (MAV), root mean square (RMS), variance (VAR), and standard deviation (SD) used to evaluate fatigue. During neural network training, a different number of hidden neurons were used, and it was found that using seven hidden neurons showed a high accuracy of 98.6%. Thus, this work indicates the potential of a low-cost system for classifying healthy and diabetic individuals using an ANN algorithm.

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