A low-cost system to control prehension force of a custom-made myoelectric hand prosthesis

Prakash, Alok; Sharma, Shiru

doi:10.1007/s42600-020-00064-w

Cited by 17 publications

(5 citation statements)

References 37 publications

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“…Reaching 75% of all anthropomorphic hands reviewed in this article, electrical motors like servos, geared motors, and linear actuators are the most popular choices for actuation as they are widely available and applicable to various joint types such as flexure and pin joints. [35][36][37][38][39][40][41][42][43][44][45][46] Pneumatic actuation is often used for soft chamber joints in robotic hands as pneumatic actuation is less likely to be used in prosthetics due to the weight and bulkiness of the pneumatic pumps. [29,47] However, there is only one example of pneumatic actuation implemented in a prosthetic hand with a compact [110] Copyright 2023, IEEE.…”

Section: Actuationmentioning

confidence: 99%

3D Printing in the Design and Fabrication of Anthropomorphic Hands: A Review

Park,

Chang,

Jung

et al. 2024

Advanced Intelligent Systems

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In this article, 3D‐printed anthropomorphic hands for prosthetic or robotic applications are reviewed as 3D printing has transformed manufacturing by enabling the creation of intricate structures layer by layer, offering design freedom and efficiency. This review categorizes 3D‐printed anthropomorphic hands based on actuation, transmission, joint, and functional features like sensing and grasp patterns. It also assesses the level of anthropomorphism and validation methods by presenting criteria in prosthetic and robotic applications. Then, the article discusses 3D printing technologies in their usage and types, highlighting the advantages of multi‐material capabilities and integration of different hand components. Future directions on structural components, anthropomorphism, validation, and use of 3D printing are discussed, focusing on trends that only 3D printing technology can achieve in anthropomorphic hand development.

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Section: Actuationmentioning

confidence: 99%

3D Printing in the Design and Fabrication of Anthropomorphic Hands: A Review

Park,

Chang,

Jung

et al. 2024

Advanced Intelligent Systems

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“…As for this research, overshoot is the most concerning parameter. High overshoot provides a more significant grasping force as the prosthetic robot arm may deform or crush the grasped object [31].…”

Section: B Controllermentioning

confidence: 99%

3D Printed Prosthetic Robot Arm with Grasping Detection System for Children

Babu¹,

Nasır²,

Ravindran³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Prosthetic robot arms have been an alternative replacement for upper limb-related disability, especially among children as it helps them perform regular activities such as holding tools, eating, and drinking. This research aims to develop a 3D prosthetic robot arm using SolidWorks and 3D printer, using a low-cost and straightforward mechanism for children. This paper proposes a close loop control system with position control for the prosthetic robot arm to achieve an appropriate grasping force focusing on solid-shaped objects using PID control. The PID controller controls the system's response to perform in the most efficient path. Force-sensitive resistors (FSR) are attached to all fingers to measure the grasping force acting on objects with different surfaces, dimensions, and weights. The controller results showed improvement in the overshoot percentage of 0.902%, as overshoot is essential in preventing the grasped object's deformations. The analysis of the experiment shows that the mean grasping force and static coefficient friction of each object are different regardless of the material the object is made of and the object's mass. For example, a cube-shaped object made of wood requires 0.5288 N of grasping force to grasp the object firmly compared to a plastic-made cube that only requires 0.3245 N to hold the cube. On the other hand, the static coefficient friction for the wood cube is 3.1708 and 0.4725 for the plastic cube. Further research can be done by designing the prosthetic robot arm with independent motorized and multi-degree movement of fingers.

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“…The Flexor Carpi Radialis (FCR) and Extensor Carpi Radialis Longus (ECRL) EMG signal values have been employed in this research to represent each wrist joint angle movement [72], [32], [73]. The electrode was placed right on top of muscle belly to ensure the signals response will represent the actual readings of muscle contraction.…”

Section: B Emg Data Collectionmentioning

confidence: 99%

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Karis,

Kasdirin,

Abas

et al. 2023

Journal of Robotics and Control

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Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer.

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A low-cost system to control prehension force of a custom-made myoelectric hand prosthesis

Cited by 17 publications

References 37 publications

3D Printing in the Design and Fabrication of Anthropomorphic Hands: A Review

3D Printing in the Design and Fabrication of Anthropomorphic Hands: A Review

3D Printed Prosthetic Robot Arm with Grasping Detection System for Children

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

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