Six Prosthetic Arm Movements Using Electromyogram Signals: A Prototype

Salama, M.; Bakr, Ahmed M.

doi:10.1109/uksim.2016.19

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…The input is compared to the values coded in the Arduino. The arm prototype performs the movement for that corresponding limit after the input is matched with the threshold value by moving the servo motor to appropriate angles [34]. For instance, if the input peak-to-peak value is between 76-80, the movement will be wrist anticlockwise.…”

Section: Resultsmentioning

confidence: 99%

Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

Ganesan¹,

Juliet²,

Joshi³

2023

Intelligent Automation &Amp; Soft Computing

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Brain signal analysis plays a significant role in attaining data related to motor activities. The parietal region of the brain plays a vital role in muscular movements. This approach aims to demonstrate a unique technique to identify an ideal region of the human brain that generates signals responsible for muscular movements; perform statistical analysis to provide an absolute characterization of the signal and validate the obtained results using a prototype arm. This can enhance the practical implementation of these frequency extractions for future neuro-prosthetic applications and the characterization of neurological diseases like Parkinson's disease (PD). To play out this handling method, electroencephalogram (EEG) signals are gained while the subject is performing different wrist and elbow movements. Then, the frontal brain signals and just the parietal signals are separated from the obtained EEG signal by utilizing a band pass filter. Then, feature extraction is carried out using Fast Fourier Transform (FFT). Subsequently, the extraction process is done by Daubechies (db4) and Haar wavelet (db1) in MATLAB and classified using the Levenberg-Marquardt Algorithm. The results of the frequency changes that occurred during various wrist movements in the parietal region are compared with the frequency changes that occurred in frontal EEG signals. This proposed algorithm also uses the deep learning pattern analysis network to evaluate the matching sequence for each action that takes place. Maximum accuracy of 97.2% and maximum error range of 0.6684% are achieved during the analysis. Results of this research confirm that the Levenberg-Marquardt algorithm, along with the newly developed deep learning hybrid PatternNet, provides a more accurate range of frequency changes than any other classifier used in previous works of literature. Based on the analysis, the peak-to-peak value is used to define the threshold for the prototype arm, which performs all the intended degrees of freedom (DOF), verifying the results. These results would aid the specialists in their decision-making by facilitating the analysis and interpretation of brain signals in the field of neuroscience, specifically in tremor analysis in PD.

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Section: Resultsmentioning

confidence: 99%

Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

Ganesan¹,

Juliet²,

Joshi³

2023

Intelligent Automation &Amp; Soft Computing

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“…Step 1: Placing one channel surface EMG (sEMG) electrodes with muscle sensor at the middle of the desired muscle for recording raw EMG signals according to the following setup [24]:  The first surface electrode must be located at the middle side of the target muscle and connected to the cable snap connector of Myoware muscle sensor.…”

Section: B the Block Diagram Of The Proposed Systemmentioning

confidence: 99%

Efficient Control System Based on Hand Nerve Signals

Abbas¹,

Croock²

2019

IJCCCE

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Recent advances in the control applications based on hand nerve signals are able to meet the needs of users who suffer from restrictions in limb movement and also provide high performance control for those paralyzed people. These signals are represented as Electromyography (EMG) signals, which are biomedical ones, used for clinical/biomedical applications. In this work, a control signals generation system is proposed based on hand EMG measurements. The process of acquisition and processing of EMG signals is performed by only one channel surface EMG electrodes with one EMG processing unit as a muscle sensor. In this work, Arduino UNO is adopted as an analog to digital converter for these hand nerve signals to be easily analyzed in the classification process. These signals are measured from the skin surface of forearm and biceps muscles in two suggested case studies to be used in generating signals based on ten muscles movements. The main features that crystallized this research is building a smart control algorithm which increases the flexibility of generating precise control signals based on contracted hand movements with high simplicity of use and the low cost. The obtained results are compared to other systems results to show the ability of achieving 93.81% classification rate or accuracy among other systems.

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“…The result achieved the challenge, which was designed to maximize functionality and performance and reduce complexity and time response. Salama and Bakr 7 designed a prototype system for moving a prosthetic lower arm, without prior operation intervention. A servo motor receives a driving signal to move the simulated arm to the required position.…”

mentioning

confidence: 99%

Design of Prosthetic Arm Based on Myoelectric Control

et al. 2021

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Absence of an upper limb leads to severe impairments in everyday life, which can further influence the social and mental state. For these reasons, early developments in cosmetic and body-driven prostheses date some centuries ago, and they have been evolving ever since. Following the end of World War II, rapid developments in technology resulted in powered myoelectric hand prosthetics. A design of prosthetic arm based on myoelectric was created. The design incorporates movement of arm in flexion and extension. The signal was acquired from biceps above the elbow joint using Ag-AgCl electrode. The proposed design contains signal conditioning circuit, microcontroller, motor drivers, and motor as part of the major components to control arm movement. Control algorithm by using a microcontroller is presented.

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Six Prosthetic Arm Movements Using Electromyogram Signals: A Prototype

Cited by 6 publications

References 13 publications

Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

Spectral Analysis and Validation of Parietal Signals for Different Arm Movements

Efficient Control System Based on Hand Nerve Signals

Design of Prosthetic Arm Based on Myoelectric Control

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