Simulation and Real-time Control of a Smart Prosthetic Hand PDF Download

Author: Amir Fassih
Publisher:
ISBN:
Category :
Languages : en
Pages : 87

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Author: Pavan Kumar Yarlagadda
Publisher:
ISBN:
Category :
Languages : en
Pages : 120

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Book Description
Surface Electro Myo Gram (sEMG) signals are important for many applications such as medical rehabilitation and prosthetic hand control. Substantial research has been conducted on finding signal processing and modeling techniques to obtain accurate estimations of joint angles and force from sEMG data. As the nature of the signal is highly noisy, most researchers apply post filtering and processing of the signal. This will increase the computational cost for the use of the sEMG signal for real-time implementation to a prosthetic hand control system. In this research, an innovative Printed Circuit Board (PCB) has been designed to capture, filter and buffer the sEMG signal. The PCB is more sensitive, robust and gives a cleaner signal when compared to traditional bread boards. The output from the board is compatible with typical microcontrollers. The designed circuit is implemented on a PCB with the dimensions of 30́+ by 50́+ for three sensors on one board. The designed circuit is used to capture real time sEMG signals, that is send to a microcontroller and processed to infer joint motion angle of specific human fingers. The processed information is used to control a finger of a prosthetic hand using servo motors.

Author: Beyda Taşar
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages :

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In recent years, researchers have conducted many studies on the design and control of prosthesis devices that take the place of a missing limb. Functional ability of prosthesis hands that mimic biological hand functions increases depending on the number of independent finger movements possible. From this perspective, in this study, six different finger movements were given to a prosthesis hand via bioelectrical signals, and the functionality of the prosthesis hand was increased. Bioelectrical signals were recorded by surface electromyography for four muscles with the help of surface electrodes. The recorded bioelectrical signals were subjected to a series of preprocessing and feature extraction processes. In order to create meaningful patterns of motion and an effective cognitive interaction network between the human and the prosthetic hand, fuzzy logic classification algorithms were developed. A five-fingered and 15-jointed prosthetic hand was designed via SolidWorks, and a prosthetic prototype was produced by a 3D printer. In addition, prosthetic hand simulator was designed in Matlab/SimMechanics. Pattern control of both the simulator and the prototype hand in real time was achieved. Position control of motors connected to each joint of the prosthetic hand was provided by a PID controller. Thus, an effective cognitive communication network established between the user, and the real-time pattern control of the prosthesis was provided by bioelectrical signals.

Author: Ankit Chaudhary
Publisher: Springer
ISBN: 9789811047978
Category : Technology & Engineering
Languages : en
Pages : 0

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This book focuses on light invariant bare hand gesture recognition while there is no restriction on the types of gestures. Observations and results have confirmed that this research work can be used to remotely control a robotic hand using hand gestures. The system developed here is also able to recognize hand gestures in different lighting conditions. The pre-processing is performed by developing an image-cropping algorithm that ensures only the area of interest is included in the segmented image. The segmented image is compared with a predefined gesture set which must be installed in the recognition system. These images are stored and feature vectors are extracted from them. These feature vectors are subsequently presented using an orientation histogram, which provides a view of the edges in the form of frequency. Thereby, if the same gesture is shown twice in different lighting intensities, both repetitions will map to the same gesture in the stored data. The mapping of the segmented image's orientation histogram is firstly done using the Euclidian distance method. Secondly, the supervised neural network is trained for the same, producing better recognition results. An approach to controlling electro-mechanical robotic hands using dynamic hand gestures is also presented using a robot simulator. Such robotic hands have applications in commercial, military or emergency operations where human life cannot be risked. For such applications, an artificial robotic hand is required to perform real-time operations. This robotic hand should be able to move its fingers in the same manner as a human hand. For this purpose, hand geometry parameters are obtained using a webcam and also using KINECT. The parameter detection is direction invariant in both methods. Once the hand parameters are obtained, the fingers’ angle information is obtained by performing a geometrical analysis. An artificial neural network is also implemented to calculate the angles. These two methods can be used with only one hand, either right or left. A separate method that is applicable to both hands simultaneously is also developed and fingers angles are calculated. The contents of this book will be useful for researchers and professional engineers working on robotic arm/hand systems.

Author: Astrid Rubiano Fonseca
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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The target of this thesis disertation is to develop a new Smart control of a soft robotic hand prosthesis for the soft robotic hand prosthesis called ProMain Hand, which is characterized by:(i) flexible interaction with grasped object, (ii) and friendly-intuitive interaction between human and robot hand. Flexible interaction results from the synergies between rigid bodies and soft bodies, and actuation mechanism. The ProMain hand has three fingers, each one is equipped with three phalanges: proximal, medial and distal. The proximal and medial are built with rigid bodies,and the distal is fabricated using a deformable material. The soft distal phalange has a new smart force sensor, which was created with the aim to detect contact and force in the fingertip, facilitating the control of the hand. The friendly intuitive human-hand interaction is developed to facilitate the hand utilization. The human-hand interaction is driven by a controller that uses the superficial electromyographic signals measured in the forearm employing a wearable device. The wearable device called MyoArmband is placed around the forearm near the elbow joint. Based on the signals transmitted by the wearable device, the beginning of the movement is automatically detected, analyzing entropy behavior of the EMG signals through artificial intelligence. Then, three selected grasping gesture are recognized with the following methodology: (i) learning patients entropy patterns from electromyographic signals captured during the execution of selected grasping gesture, (ii) performing a support vector machine classifier, using raw entropy data extracted in real time from electromyographic signals.

Author: Jagdish Lal Raheja
Publisher:
ISBN: 9789533079417
Category :
Languages : en
Pages :

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Author: Mario Alejandro Benítez López
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
Control of myoelectric prosthetic hands is still an open problem, currently, most commercial prostheses use direct, proportional or Finite State Machine control for this purpose. However, as mechanical design advances, more dexterous prostheses with more degrees of freedom (DOF) are created, then a more precise and intuitive control for the user is required. State of the art has focused in the use of pattern recognition as a control strategy with promising results. Studies have shown similar results to classic control strategies with the advantage of being more intuitive for the user. Many works have tried to find the algorithms that best follows the user?s intention. However, deployment of these algorithms for realtime classification in a prosthesis has not been widely explored. This project addresses this problem by deploying and testing in real-time an artificial neural network (ANN). The ANN was trained to classify three different motions: no grasp, precision grasp and power grasp in order to control a two DOF trans-radial prosthetic hand with electromyographic signals acquired from two channels. Static and dynamic tests were made to evaluate the ANN under those conditions, 95% and 81% accuracy scores were reached respectively. Our work shows the potential of pattern recognition algorithms to be deployed in microcontrollers that can fit inside myoelectric prostheses. On the other hand, a prototype of a prosthetic hand that is able to physically replicate the classified actions was developed.

Author: Luenin Adriel Barrios
Publisher:
ISBN:
Category :
Languages : en
Pages : 178

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The design and development of a real-time computer program for controlling the anthropomorphic six degrees of freedom robotic hand developed in the Robotics Laboratory at San Diego State University is presented. The program allows the hand to be controlled either manually or via EMG signals received from electrodes attached to the user's forearm. The robot hand's joints can be operated independently or ordered to perform synergetic grasp motions. In particular, this research continues the previous work conducted at the lab of using feature extraction and classification of EMG signals in real-time to determine the user's hand position and command the robot hand to achieve the same motion. The grasp modes studied include: cylindrical, spherical, point, and lateral hand positions, and a first order rational approximation function is used to achieve the synergetic motions of the robot hand. The results demonstrate that feature extraction and classification of EMG signals serve as an adequate method for controlling the motions of a robot hand and that such control systems may be utilized in areas such as prostheses.

Author: Cheng-Hung Chen
Publisher: John Wiley & Sons
ISBN: 1119273595
Category : Technology & Engineering
Languages : en
Pages : 256

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Book Description
An in-depth review of hybrid control techniques for smart prosthetic hand technology by two of the world’s pioneering experts in the field Long considered the stuff of science fiction, a prosthetic hand capable of fully replicating all of that appendage’s various functions is closer to becoming reality than ever before. This book provides a comprehensive report on exciting recent developments in hybrid control techniques—one of the most crucial hurdles to be overcome in creating smart prosthetic hands. Coauthored by two of the world’s foremost pioneering experts in the field, Fusion of Hard and Soft Control Strategies for Robotic Hand treats robotic hands for multiple applications. Itbegins withan overview of advances in main control techniques that have been made over the past decade before addressing the military context for affordable robotic hand technology with tactile and/or proprioceptive feedback for hand amputees. Kinematics, homogeneous transformations, inverse and differential kinematics, trajectory planning, and dynamic models of two-link thumb and three-link index finger are discussed in detail. The remainder of the book is devoted to the most promising soft computing techniques, particle swarm optimization techniques, and strategies combining hard and soft controls. In addition, the book: Includes a report on exciting new developments in prosthetic/robotic hand technology, with an emphasis on the fusion of hard and soft control strategies Covers both prosthetic and non-prosthetic hand designs for everything from routine human operations, robotic surgery, and repair and maintenance, to hazardous materials handling, space applications, explosives disposal, and more Provides a comprehensive overview of five-fingered robotic hand technology kinematics, dynamics, and control Features detailed coverage of important recent developments in neuroprosthetics Fusion of Hard and Soft Control Strategies for Robotic Hand is a must-read for researchers in control engineering, robotic engineering, biomedical sciences and engineering, and rehabilitation engineering.

Author: Michelle M. Lusardi
Publisher: Butterworth-Heinemann
ISBN:
Category : Medical
Languages : en
Pages : 936

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Book Description
Whether you are a student or a clinician, if you work with patients with neuromuscular and musculoskeletal impairments, you will find this text supplies a strong foundation in and appreciation for the field of orthotics and prosthetics that will give you the critical skills you need when working with this unique client population.