Electromyography Feature Analysis to Recognize the Hand Motion in a Prosthetic Hand Design

2021 ◽  
Vol 50 ◽  
pp. 25-37
Author(s):  
T. Triwiyanto ◽  
Endro Yulianto ◽  
I Dewa Gede Hari Wisana ◽  
Muhammad Ridha Mak’ruf ◽  
Bambang Guruh Irianto ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Time Domain ◽  
Euclidean Distance ◽  
Prosthetic Hand ◽  
Hand Motion ◽  
Prosthetic Hands ◽  
Emg Signal ◽  
Dry Electrode ◽  
Flexion Extension ◽  
Selection Of

The increasing need for prosthetic hands for people with disabilities is one reason for innovation in the field of prosthetic hands to create the best prosthetic hand technology. In the design of EMG-based prosthetic hands, this is determined by several things, among others, the selection of features. The selection of the right features will determine the accuracy of the prosthetic hand Therefore, the purpose of this study is to analysis the time domain feature to obtain the best feature in classifying the hand motion. The contribution of this work is able to detect 4 movements in real time, namely hand close, flexion, extension, and relax. The Electromyograph signal is tapped using an electromyograph (EMG) dry electrode sensor in which there is a circuit of EMG instrumentation amplifier. Furthermore, the analog EMG signal data is processed through the ADC (Analog to Digital Converter) by using MCP3008 device. EMG signal data is processed in Raspberry Pi. A feature extraction process is applied to reduce data and determine the characteristics of each hand movement. Feature extraction used is MAV (mean absolute value), SSI (sign slope integral), VAR (variance), and RMS (root mean square). From the results of the four-time domain feature, then the best feature extraction is determined using scatter plot and Euclidean distance. The results that have been carried out on ten people with each person doing ten sets of movements (hand close, flexion, extension, relax), showing the best Euclidean distance results, is the RMS feature, with a value of 2608.07. This data is the result of the best feature extraction analysis through the method of calculating the distance of feature extraction data using Euclidean distance. This analysis of time domain feature is expected to be useful for further experiment in machine learning implementation so that it can be obtained an effective prosthetic hand.

scholarly journals The Development of Body-Powered Prosthetic Hand Controlled by EMG Signals Using DSP Processor with Virtual Prosthesis Implementation

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Fathia H. A. Salem ◽  
Khaled S. Mohamed ◽  
Sundes B. K. Mohamed ◽  
Amal A. El Gehani
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
The Body ◽  
Prosthetic Hand ◽  
Prosthetic Hands ◽  
Emg Signal ◽  
Mechanical Hand ◽  
Fast Processing ◽  
Opening And Closing ◽  
Two Stages

The state of the art in the technology of prosthetic hands is moving rapidly forward. However, there are only two types of prosthetic hands available in Libya: the Passive Hand and the Mechanical Hand. It is very important, therefore, to develop the prosthesis existing in Libya so that the use of the prosthesis is as practical as possible. Considering the case of amputation below the elbow, with two movements: opening and closing the hand, this work discusses two stages: developing the operation of the body-powered prosthetic hand by controlling it via the surface electromyography signal (sEMG) through dsPIC30f4013 processor and a servo motor and a software based on fuzzy logic concept to detect and process the EMG signal of the patient as well as using it to train the patient how to control the movements without having to fit the prosthetic arm. The proposed system has been practically implemented, tested, and gave satisfied results, especially that the used processor provides fast processing with high performance compared to other types of microcontrollers.

State-of-the-Art Method in Prosthetic Hand Design: A Review

2021 ◽  
Vol 50 ◽  
pp. 15-24
Author(s):  
T. Triwiyanto ◽  
Triana Rahmawati ◽  
I. Putu Alit Pawana ◽  
L. Lamidi ◽  
Torib Hamzah ◽  
...  
Keyword(s):  
Low Power ◽  
Low Cost ◽  
Maximum Voluntary Contraction ◽  
Voluntary Contraction ◽  
Occupational Accidents ◽  
Limb Amputation ◽  
Prosthetic Hand ◽  
Advantages And Disadvantages ◽  
Prosthetic Hands ◽  
Emg Signal

Human limb amputation can be caused due to congenital disabilities, accidents, and certain diseases. Amputation caused by occupational accidents is a frequent occurrence in developing countries. Meanwhile, amputation caused by certain diseases such as diabetes Miletus is also the leading cause. The need for prosthetic hand is increasing along with the increase in those two factors. Several researchers have developed prosthetic hands with advantages and disadvantages. Research on prosthetic hands, which are useful, low power, and low cost, is still a major issue. Therefore, the purpose of this paper is to provide a review of the various designs of prosthetic hands, specifically on the sensor, control, and actuator systems. This paper collected several references from proceedings and journals related to the design of the prosthetic hand. The results show that the EMG signal is widely used by some researchers in controlling prosthetic hands compared to other sensors, following the force-sensitive resistor (FSR) sensor. To control prosthetic hands, some researchers used a threshold system with a value of 20% of the maximum voluntary contraction (MVC), and several other researchers used a pattern recognition model based on the EMG signal feature. Moreover, In the mechanical part, the open-source prosthetic hand model is more widely used than the fabricate prosthetic hand. This is due to the cost required in the prosthetic hand design is cheaper than a fabricated one. The results of this review are expected to provide a recommendation to researchers in the development of low cost, low power, and practical prosthetic hands.

EEG-Based Motor Imagery Feature Extraction

2013 ◽  
Vol 846-847 ◽  
pp. 944-947
Author(s):  
Yang Liu ◽  
Nian Qiang Li ◽  
Yong Xiang Li
Keyword(s):  
Feature Extraction ◽  
Motor Imagery ◽  
Time Domain ◽  
Classification Accuracy ◽  
Power Spectrum Density ◽  
Continuous Wavelet ◽  
Spectrum Density ◽  
Energy Feature ◽  
The Time Domain ◽  
Selection Of

In this study, we proposed a simple and effective approach for feature extraction of motor imagery (MI) data. Aside from the original use of continuous wavelet transform (CWT), the Blackman filter is proposed to further refine the selection of active segments. In the time domain we compute the energy feature by squared-amplitude of EEG; in the frequency domain BT method power spectrum density (PSD) is used to get energy feature. The method is simple and the classification accuracy is satisfactory, especially for classification 2.

scholarly journals IMPLEMENTACIÓN EN FPGA DE UN CLASIFICADOR DE MOVIMIENTOS DE LA MANO USANDO SEÑALES EMG

2015 ◽  
Vol 6 (1) ◽  
pp. 85 ◽  
Author(s):  
David Alexander Reyes Lopez ◽  
Mauricio Arias López ◽  
Jorge Enrique Duarte Sánchez ◽  
Humberto Loaiza Correa
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Processing System ◽  
Wrist Flexion ◽  
Identification Rate ◽  
Design And Implementation ◽  
Emg Signal ◽  
Flexion Extension ◽  
El Sistema

Este trabajo presenta el diseño e implementación de un clasificador de señales electromiográficas (EMG) para tres movimientos de la mano: flexión, extensión y cierre, usando dos músculos del antebrazo, palmar largo y extensor común de los dedos. El desarrollo comprende dos bloques principales, el hardware para la adquisición y adecuación de la señales EMG analógicas y el sistema de procesamiento para la identificación y clasificación del movimiento realizado; el sistema completo fue implementado en hardware usando un kit de desarrollo DE2-70 que cuenta con un FPGA Cyclone II de Altera. Para la extracción de características se implementó la Transformada Rápida de Fourier (FFT), para cada canal, a la cual se le calcularon  técnicas de procesamiento  como la varianza y el promedio.. Finalmente, se establece un umbral de decisión para identificar el movimiento realizado. El tiempo de respuesta del sistema total fue de 17,7 us, obteniendo una tasa de identificación mayor al 87%.FPGA implementation of a hand motions classifier using EMG signalsAbstractThis paper presents the design and implementation of a hand motions classifier using electromyographic (EMG) signals. The classified motions are: wrist flexion, wrist extension and hand closure. The motions are classified using two forearm muscles: longus palmar and extensor digitorum. This work was implemented in two principal blocks: the acquisition and adequacy of the EMG signal, and the processing system for the identification and classification of the motion made. The processing system was implemented on hardware using a development kit with a Cyclone II FPGA from Altera. For the feature extraction the Fast Fourier Transform (FFT) is performed at each channel and some features like variance and mean are calculated. Finally, a threshold decision block is used to identify the motion. The system have a time response of 17,7 us, obtaining an identification rate higher than 87%.Keywords: EMG signals, FPGA, motion classifier, real time.

scholarly journals Denoising and Analysis of EMG Signal using Wavelet Transform

2020 ◽  
pp. 13-19
Author(s):  
Iffat Ara
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Time Domain ◽  
Spectrum Analysis ◽  
Surface Electromyography ◽  
Additive Noise ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Emg Signal ◽  
Classification Time

EMG is the recording of the electrical activity produced within the muscle fibers. Measurement of EMG signal is corrupted by additive noise whose signal-to-noise ratio (SNR) varies. Feature extraction is an important step for EMG classification. Time domain and frequency domain parameters were chosen as representative features for EMG signals. In this thesis, the Wavelet transform and wavelet coefficients have adopted to represent the EMG signals. Wavelet transform (WT) has been applied also in this research for the analysis of the surface electromyography signal (SEMG). The properties of wavelet transform turned out to be suitable for nonstationary EMG signals. Also Spectrum analysis has been applied to various types of EMG signal.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

scholarly journals Recent Advances in Myoelectric Control for Finger Prostheses for Multiple Finger Loss

2021 ◽  
Vol 11 (10) ◽  
pp. 4464
Author(s):  
Viritpon Srimaneepong ◽  
Artak Heboyan ◽  
Azeem Ul Yaqin Syed ◽  
Hai Anh Trinh ◽  
Pokpong Amornvit ◽  
...  
Keyword(s):  
Web Of Science ◽  
Myoelectric Control ◽  
Original Research ◽  
Prosthetic Hand ◽  
Precise Control ◽  
Prosthetic Hands ◽  
Prosthesis Retention ◽  
Finger Prosthesis ◽  
Artificial Hand

The loss of one or multiple fingers can lead to psychological problems as well as functional impairment. Various options exist for replacement and restoration after hand or finger loss. Prosthetic hand or finger prostheses improve esthetic outcomes and the quality of life for patients. Myoelectrically controlled hand prostheses have been used to attempt to produce different movements. The available articles (original research articles and review articles) on myoelectrically controlled finger/hand prostheses from January 1922 to February 2021 in English were reviewed using MEDLINE/PubMed, Web of Science, and ScienceDirect resources. The articles were searched using the keywords “finger/hand loss”, “finger prosthesis”, “myoelectric control”, and “prostheses” and relevant articles were selected. Myoelectric or electromyography (EMG) signals are read by myoelectrodes and the signals are amplified, from which the muscle’s naturally generated electricity can be measured. The control of the myoelectric (prosthetic) hands or fingers is important for artificial hand or finger movement; however, the precise control of prosthetic hands or fingers remains a problem. Rehabilitation after multiple finger loss is challenging. Implants in finger prostheses after multiple finger loss offer better finger prosthesis retention. This article presents an overview of myoelectric control regarding finger prosthesis for patients with finger implants following multiple finger loss.

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