Design and Deployment of Grasp Control System applied to robotic hand prosthesis

Calderón, Carlos; Ramírez, Cristián; Barros, Veronica; Punin, Gabriela

doi:10.1109/tla.2017.7854610

Cited by 37 publications

(13 citation statements)

References 7 publications

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“…At the same time, the generalized regression neural network is used for classification and recognition, and the classification results are compared with the support vector machine. At the same time, in order to make the GRNN result more accurate, the FOA is used to find the best smoothing factor Spread for the kernel parameters of the generalized regression network [56]. The target function of the FOA optimization uses the root mean square error (RMSE) of the predicted value of the classification results and the actual value.…”

Section: The Establishment Of Generalized Regression Neural Network Mmentioning

confidence: 99%

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Jiang

et al. 2018

Neural Comput & Applic

103

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In the current development and design of sports rehabilitation equipment or biomimetic prostheses, in addition to pay attention to the development and design of the structure, the more core is how to realize the accurate and effective control of the rehabilitation equipment or intelligent prosthesis, and the current research is based on data process and pattern recognition. This paper designs 9 kinds of actions that can react effectively to the function of the hand and extracts the original EMG signals, which are based on the sEMG of the forearm muscles of human hand movement, and uses the 20 order comb filter and wavelet threshold to preprocess the signal, and uses the root mean square, wavelength and nonlinear characteristics sample entropy in time domain as three eigenvalues to construct the input feature vectors of the subsequent action classifier. Finally, the recognition of the hand movements is realized successfully through GRNN and SVM. The recognition rate is 98.64% in SVM classifier and 96.27% in GRNN classifier. Experimental results show that the SVM classifier is better than the GRNN classifier.

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Section: The Establishment Of Generalized Regression Neural Network Mmentioning

confidence: 99%

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Jiang

et al. 2018

Neural Comput & Applic

103

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“…The proposed robotic hand consists of four fingers on each hand and 12 joints that consist of linked knuckles and linear actuators. Under the project “Hand of Hope,” low-cost robotic hand prostheses with an efficient grasp control mechanism were developed that can perform object catch function [ 73 ]. In another study, a crude robotic hand has been proposed that resembles the human hand [ 74 ].…”

Section: Application Of Iot-aided Robotic Technology In Healthcarementioning

confidence: 99%

Internet of Things and Robotics in Transforming Current-Day Healthcare Services

Pradhan

Bharti

Chakravarty

et al. 2021

Journal of Healthcare Engineering

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Technology has become an integral part of everyday lives. Recent years have witnessed advancement in technology with a wide range of applications in healthcare. However, the use of the Internet of Things (IoT) and robotics are yet to see substantial growth in terms of its acceptability in healthcare applications. The current study has discussed the role of the aforesaid technology in transforming healthcare services. The study also presented various functionalities of the ideal IoT-aided robotic systems and their importance in healthcare applications. Furthermore, the study focused on the application of the IoT and robotics in providing healthcare services such as rehabilitation, assistive surgery, elderly care, and prosthetics. Recent developments, current status, limitations, and challenges in the aforesaid area have been presented in detail. The study also discusses the role and applications of the aforementioned technology in managing the current pandemic of COVID-19. A comprehensive knowledge has been provided on the prospect of the functionality, application, challenges, and future scope of the IoT-aided robotic system in healthcare services. This will help the future researcher to make an inclusive idea on the use of the said technology in improving the healthcare services in the future.

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“…However, this achievement has proven to be very difficult due to space, energy, and other physical restrictions. Recent solutions to this problem have involved reducing the number of fingers [2], locating the actuators outside the hand to achieve a similar amount of DoF in the robot [22], [23], transforming and reducing the number of DoF [3], [4], [16], [24].…”

Section: Hand Designmentioning

confidence: 99%

“…Table 1 presents a summary of some of those works with their main characteristics and achievements. The table includes the following key points: (1) control scheme or strategy for grasping, (2) finger gear or type of transmission used for the fingers movement, (3) the type of actuators used, (4) the quantity and type of sensors Calderon et al [3] PID control Four-bar linkage 5 DC motors 5 force sensors PC -5…”

Section: Introductionmentioning

confidence: 99%

Development of a Robotic Hand Using Bioinspired Optimization for Mechanical and Control Design: UnB-Hand

2021

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For the last four decades, the development of robotic hands has been the focus of several works. However, a small part of those approaches consider the exploitation of parallelism of FPGAbased (Field Programmable Gate Arrays) systems or discuss how using bioinspired optimization algorithms could improve the mechanical and controller components. This work considers developing a bioinspired robotic hand that achieves motion and force control with a logic hardware architecture implemented in FPGA intended to be replicated and executed with suitable parallelism, fitting a single device. The developed robotic hand prototype has five fingers and seven DoF (Degrees of Freedom). Using bioinspired optimization, such as PSO (Particle Swarm Optimization), both the rigid finger mechanism and the impedance controller were optimized and incorporated the results in several practical grasping experiments. The validation of this work is done with the Cutkosky grasping taxonomy and some grasping experiments with interference. The tests proved the proficiency of this works for a wide range of power and some precision grasp. The reader can see the experiments in the attached videos.

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Design and Deployment of Grasp Control System applied to robotic hand prosthesis

Cited by 37 publications

References 7 publications

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Internet of Things and Robotics in Transforming Current-Day Healthcare Services

Development of a Robotic Hand Using Bioinspired Optimization for Mechanical and Control Design: UnB-Hand

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