EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm

Taşar, Beyda; Gülten, Arif

doi:10.5772/intechopen.68242

Cited by 5 publications

(3 citation statements)

References 30 publications

(34 reference statements)

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“…This difficulty can be solved by extending the fuzzy-logic principle to detect gait phases for fuzzy membership values [14]. Researchers have used fuzzy-logic techniques to control upper limbs [15] and lower limb prostheses [16]. Among these studies, a few researchers worked on image-based control [17], whereas others have used wearable sensors such as Electromyography (EMG), IMU sensors, FSR sensors on foot insole, and knee and ankle angle sensors [18].…”

Section: Introductionmentioning

confidence: 99%

A Standalone Real-Time Gait Phase Detection Using Fuzzy-Logic Implementation in Arduino Nano

et al. 2021

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This paper presents a real-time standalone computing system's design and development using an Arduino Nano controller for human gait phase detection. It acquires the lower limb segment angles using 3-inertial measurement units and foot insole-based force data using 2-force sensitive resistor sensors for each leg. Arduino IDE-based algorithms are designed to get thigh, shank, and foot angles using the I2C protocol. The force information is achieved using an insole that gathers data from the heel and toe regions. A fuzzy logic-based algorithm is developed in the Arduino IDE for the detection of 7-gait phases. MATLAB's neuro-fuzzy designer toolbox is utilized to obtain a rule base for these gait phases. The results showed that the present system gives a prediction accuracy of 89.65 (± 3)% that is good enough, along with a 20 ms prediction time to make it real-time gait phase detection. This study finds the application of a fuzzy-logic algorithm for real-time control of the ankle-foot prosthesis in an open space using a standalone system with an amputee.

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Section: Introductionmentioning

confidence: 99%

A Standalone Real-Time Gait Phase Detection Using Fuzzy-Logic Implementation in Arduino Nano

et al. 2021

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“…Most of the works done by pioneers in the field of prosthetics to map muscle signals to joint actuations in prosthetic arm are based on Pattern Recognition and Classification, that come under Supervised Machine Learning. Taşar, B., & Gülten, A. (2017) used Fuzzy logic based classification method to map the forearm muscle movements to joint movements of a bionic arm, using surface electrodes [20,21,22].…”

Section: Introductionmentioning

confidence: 99%

Bionic arm: Mapping of elbow and wrist flexion using neural network and fuzzy logic

Dash

2021

JER is an international, peer-reviewed journal that publishes f

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Cases on limb amputation necessitate the use of Transhumeral bionic for artificial limb rehabilitation, which is controlled using Electromyographic (EMG) signals from the muscles. Before the implementation of EMG control, a mapping between the movements of an arm to the angle formed at the corresponding joints is essential to be made. Most of the works in the field of Bionics use Supervised Machine Learning models, chiefly Classification, to map muscle flexion signals to joint actuations in the bionic arm. Ample literature is also there, which uses fuzzy logic for mapping. However, there are very few literatures that compare these two methods of mapping. In this article, 2 models have been discussed regarding the mapping, and their effectiveness is compared. The first model captures elbow and wrist flexion and maps them to their respective angular displacements of joints using a fuzzy logic model. In the second model, a Pattern Recognition Artificial Neural Network (ANN) model under Supervised Machine Learning is incorporated to map elbow and wrist flexion to the corresponding joint angular displacement. The ANN is trained with elbow and wrist joint flexion values and its corresponding joint angles data, optimized, and tested in real-time. This model is verified by comparing the joint angles of a test person (measured using Goniometers) with the joint angles of Bionic models made (using a 360° protractor sheet). The second model gave the insight that supervised machine learning models provide an accurate mapping to the joint flexion in the field of bionics.

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“…Chu et al, 2005). (B Taşar et al, 2017). In this article; mechanical design, modelling and position control of prosthetic hand issues were presented.…”

Section: Introductionmentioning

confidence: 99%

Modeling, Simulation and Control of Prosthetic Hand using Sim Mechanics

Taşar¹,

Gülten²,

Yakut³

2018

IJCET

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In recent years, researches about design and control of prosthetic, orthotics devices have been developed rapidly. Modeling and testing process of these devices requires long time. Matlab/SimMechanics Toolbox is a very effective and easy for modeling and analyzing of mechanical system. This paper presents the multi body simulation, analysis and control of 15 DoF prosthetic hand. Prosthetic hand 3D model is designed using SolidWorks, according to structural of the adult human hand. Hand pattern are demonstrated via MATLAB SimMechanics toolbox. PID controller response for each joints of fingers are presented.

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EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm

Cited by 5 publications

References 30 publications

A Standalone Real-Time Gait Phase Detection Using Fuzzy-Logic Implementation in Arduino Nano

A Standalone Real-Time Gait Phase Detection Using Fuzzy-Logic Implementation in Arduino Nano

Bionic arm: Mapping of elbow and wrist flexion using neural network and fuzzy logic

Modeling, Simulation and Control of Prosthetic Hand using Sim Mechanics

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