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Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques
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Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducted in this study utilized the Binary Grey Wolf Optimization (BGWO) algorithm to select optimal features for the proposed classification model. The results demonstrate promising outcomes, with an average classification accuracy of 93.6% for three amputees and five individuals with intact limbs. The accuracy achieved in classifying the seven types of hand and wrist movements further validates the effectiveness of the proposed approach. By offering a non-invasive and reliable means of recognizing upper limb movements, this research represents a significant step forward in biotechnical engineering for upper limb amputees. The findings hold considerable potential for enhancing the control and usability of prosthetic devices, ultimately contributing to the overall quality of life for individuals with upper limb amputations.

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Publication Date
Mon Apr 07 2025
Journal Name
Al-nahrain Journal For Engineering Sciences
Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification
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Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio

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Publication Date
Mon Mar 14 2022
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Mathematical simulation of memristive for classification in machine learning
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Publication Date
Wed Dec 13 2017
Journal Name
Al-khwarizmi Engineering Journal
Ant Colony Optimization Based Force-Position Control for Human Lower Limb Rehabilitation Robot
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The aim of human lower limb rehabilitation robot is to regain the ability of motion and to strengthen the weak muscles. This paper proposes the design of a force-position control for a four Degree Of Freedom (4-DOF) lower limb wearable rehabilitation robot. This robot consists of a hip, knee and ankle joints to enable the patient for motion and turn in both directions. The joints are actuated by Pneumatic Muscles Actuators (PMAs). The PMAs have very great potential in medical applications because the similarity to biological muscles. Force-Position control incorporating a Takagi-Sugeno-Kang- three- Proportional-Derivative like Fuzzy Logic (TSK-3-PD) Controllers for position control and three-Proportional (3-P) controllers for force contr

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Oil spill classification based on satellite image using deep learning techniques
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 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

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Publication Date
Sat Jun 01 2024
Journal Name
Journal Of Ecological Engineering
Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
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Publication Date
Mon May 06 2024
Journal Name
Journal Of Ecological Engineering
Using Machine Learning Algorithms to Predict the Sweetness of Bananas at Different Drying Times
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The consumption of dried bananas has increased because they contain essential nutrients. In order to preserve bananas for a longer period, a drying process is carried out, which makes them a light snack that does not spoil quickly. On the other hand, machine learning algorithms can be used to predict the sweetness of dried bananas. The article aimed to study the effect of different drying times (6, 8, and 10 hours) using an air dryer on some physical and chemical characteristics of bananas, including CIE-L*a*b, water content, carbohydrates, and sweetness. Also predicting the sweetness of dried bananas based on the CIE-L*a*b ratios using machine learn- ing algorithms RF, SVM, LDA, KNN, and CART. The results showed that increasing the drying

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Publication Date
Sat Dec 05 2015
Journal Name
PrzeglĄd Elektrotechniczny
Power Amplifier Frequency Controller Using feedback control techniques for Bio-implanted Devices
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Publication Date
Fri Mar 01 2024
Journal Name
Al-khwarizmi Engineering Journal
Control System Development of Cap-Seal Assembling Machine
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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
PAPR Reduction of OFDM Signals Using Clipping and Coding
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The problem of the high peak to average ratio (PAPR) in OFDM signals is investigated with a brief presentation of the various methods used to reduce the PAPR with special attention to the clipping method. An alternative approach of clipping is presented, where the clipping is performed right after the IFFT stage unlike the conventional clipping that is performed in the power amplifier stage, which causes undesirable out of signal band spectral growth. In the proposed method, there is clipping of samples not clipping of wave, therefore, the spectral distortion is avoided. Coding is required to correct the errors introduced by the clipping and the overall system is tested for two types of modulations, the QPSK as a constant amplitude modul

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Publication Date
Fri Mar 19 2021
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
New cost control techniques in mega construction projects
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