Control of Hand Prostheses: A Literature Review

2013 ◽  
Author(s):  
Aimee Cloutier ◽  
James Yang
Keyword(s):  
Nervous System ◽  
Sensory Feedback ◽  
Recent Progress ◽  
Control Method ◽  
Control Strategies ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Natural Control ◽  
Prosthetic Hands ◽  
And Control

In recent years, there has been a steep rise in the quality of prostheses for patients with upper limb amputations. One common control method, using electromyographic (EMG) signals generated by muscle contractions, has allowed for an increase in the degrees of freedom (DOFs) of hand designs and a larger number of available grip patterns with little added complexity for the wearer. However, it provides little sensory feedback and requires non-natural control which must be learned by the user. Another recent improvement in prosthetic hand design instead employs electroneurographic (ENG) signals, requiring an interface directly with the peripheral nervous system (PNS) or the central nervous system (CNS) to control a prosthetic hand. While ENG methods are more invasive than using surface EMG for control, an interface with the PNS has the potential to provide more natural control and creates an avenue for both efferent and afferent sensory feedback. Despite the recent progress in design and control strategies, however, prosthetic hands are still far more limited than the actual human hand. This review outlines the recent progress in the development of EMG and ENG controlled prosthetic hands, discussing advancements in the areas of sensory feedback and control. The potential benefits and limitations of both control strategies, in terms of signal classification, invasiveness, and sensory feedback, are examined. A brief overview of interfaces with the CNS is provided, and potential future developments for these control methods are discussed.

scholarly journals Design and multichannel electromyography system-based neural network control of a low-cost myoelectric prosthesis hand

2021 ◽  
Vol 12 (1) ◽  
pp. 69-83
Author(s):  
Saygin Siddiq Ahmed ◽  
Ahmed R. J. Almusawi ◽  
Bülent Yilmaz ◽  
Nuran Dogru
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Low Cost ◽  
Hand Movement ◽  
Cost Effective ◽  
Network Control ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Myoelectric Prosthesis ◽  
System File

Abstract. This study introduces a new control method for electromyography (EMG) in a prosthetic hand application with a practical design of the whole system. The hand is controlled by a motor (which regulates a significant part of the hand movement) and a microcontroller board, which is responsible for receiving and analyzing signals acquired by a Myoware muscle device. The Myoware device accepts muscle signals and sends them to the controller. The controller interprets the received signals based on the designed artificial neural network. In this design, the muscle signals are read and saved in a MATLAB system file. After neural network program processing by MATLAB, they are then applied online to the prosthetic hand. The obtained signal, i.e., electromyogram, is programmed to control the motion of the prosthetic hand with similar behavior to a real human hand. The designed system is tested on seven individuals at Gaziantep University. Due to the sufficient signal of the Mayo armband compared to Myoware sensors, Mayo armband muscle is applied in the proposed system. The discussed results have been shown to be satisfactory in the final proposed system. This system was a feasible, useful, and cost-effective solution for the handless or amputated individuals. They have used the system in their day-to-day activities that allowed them to move freely, easily, and comfortably.

scholarly journals Modeling and Analysis of a Visual Feedback System to Support Efficient Object Grasping of an EMG-Controlled Prosthetic Hand

2019 ◽  
Vol 5 (1) ◽  
pp. 207-210
Author(s):  
Tolgay Kara ◽  
Ahmad Soliman Masri
Keyword(s):  
Visual Feedback ◽  
Low Cost ◽  
Feedback System ◽  
Hand Gesture ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Modeling And Analysis ◽  
Prosthetic Hands ◽  
Prosthetic Limbs ◽  
Computer Environment

AbstractMillions of people around the world have lost their upper limbs mainly due to accidents and wars. Recently in the Middle East, the demand for prosthetic limbs has increased dramatically due to ongoing wars in the region. Commercially available prosthetic limbs are expensive while the most economical method available for controlling prosthetic limbs is the Electromyography (EMG). Researchers on EMG-controlled prosthetic limbs are facing several challenges, which include efficiency problems in terms of functionality especially in prosthetic hands. A major issue that needs to be solved is the fact that currently available low-cost EMG-controlled prosthetic hands cannot enable the user to grasp various types of objects in various shapes, and cannot provide the efficient use of the object by deciding the necessary hand gesture. In this paper, a computer vision-based mechanism is proposed with the purpose of detecting and recognizing objects and applying optimal hand gesture through visual feedback. The objects are classified into groups and the optimal hand gesture to grasp and use the targeted object that is most efficient for the user is implemented. A simulation model of the human hand kinematics is developed for simulation tests to reveal the efficacy of the proposed method. 80 different types of objects are detected, recognized, and classified for simulation tests, which can be realized by using two electrodes supplying the input to perform the action. Simulation results reveal the performance of proposed EMG-controlled prosthetic hand in maintaining optimal hand gestures in computer environment. Results are promising to help disabled people handle and use objects more efficiently without higher costs.

Study on Current Detecting and Control Method for STATCOM across Transformer

2011 ◽  
Vol 130-134 ◽  
pp. 1933-1937
Author(s):  
Cun Ping Wang ◽  
Xiang Gen Yin ◽  
Jian Liu ◽  
Qing Xiong
Keyword(s):  
Reactive Power ◽  
Control Method ◽  
Electronic Device ◽  
Control Strategies ◽  
Power Electronic ◽  
Output Current ◽  
Load Current ◽  
Decoupling Method ◽  
Reactive Current ◽  
And Control

Static var compensator (STATCOM) is a widely used power electronic device for dynamic reactive power compensation, and its current detecting and control method generally adopts d-q decoupling method based on the synchronous rotating coordinate transformation. However, in the traditional method, the load reactive current component is directly used as the command of STATCOM output current, so it is required that the installation position of STATCOM must be consistent with load current detecting point. But in practice, STATCOM installation point and load current detecting point may be located at two different sides of the transformer. For this situation, this paper proposed an improved reactive current detecting and control method based on the principle of power balance, achieving the current detecting and control strategies when the two points are located at different sides of transformer. Finally, the simulation of system with STATCOM accessed from the third winding of transformer verifies the correctness and feasibility of the proposed method.

scholarly journals Advantages of comparative studies in songbirds to understand the neural basis of sensorimotor integration

2017 ◽  
Vol 118 (2) ◽  
pp. 800-816 ◽  
Author(s):  
Karagh Murphy ◽  
Logan S. James ◽  
Jon T. Sakata ◽  
Jonathan F. Prather
Keyword(s):  
Nervous System ◽  
Sensory Feedback ◽  
Sensorimotor Integration ◽  
New Technologies ◽  
Sensory Information ◽  
Neural Basis ◽  
Communication Behaviors ◽  
Song Repertoire ◽  
Evolutionary Variation ◽  
And Control

Sensorimotor integration is the process through which the nervous system creates a link between motor commands and associated sensory feedback. This process allows for the acquisition and refinement of many behaviors, including learned communication behaviors such as speech and birdsong. Consequently, it is important to understand fundamental mechanisms of sensorimotor integration, and comparative analyses of this process can provide vital insight. Songbirds offer a powerful comparative model system to study how the nervous system links motor and sensory information for learning and control. This is because the acquisition, maintenance, and control of birdsong critically depend on sensory feedback. Furthermore, there is an incredible diversity of song organizations across songbird species, ranging from songs with simple, stereotyped sequences to songs with complex sequencing of vocal gestures, as well as a wide diversity of song repertoire sizes. Despite this diversity, the neural circuitry for song learning, control, and maintenance remains highly similar across species. Here, we highlight the utility of songbirds for the analysis of sensorimotor integration and the insights about mechanisms of sensorimotor integration gained by comparing different songbird species. Key conclusions from this comparative analysis are that variation in song sequence complexity seems to covary with the strength of feedback signals in sensorimotor circuits and that sensorimotor circuits contain distinct representations of elements in the vocal repertoire, possibly enabling evolutionary variation in repertoire sizes. We conclude our review by highlighting important areas of research that could benefit from increased comparative focus, with particular emphasis on the integration of new technologies.

scholarly journals Next-Generation Prosthetic Hand: from Biomimetic to Biorealistic

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-4
Author(s):  
Ning Lan ◽  
Manzhao Hao ◽  
Chuanxin M. Niu ◽  
He Cui ◽  
Yu Wang ◽  
...  
Keyword(s):  
Sensory Information ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Grand Challenge ◽  
Sensorimotor System ◽  
Next Generation ◽  
Ongoing Research ◽  
Sensory Awareness ◽  
Prosthetic Hands ◽  
Significant Step

Integrating a prosthetic hand to amputees with seamless neural compatibility presents a grand challenge to neuroscientists and neural engineers for more than half century. Mimicking anatomical structure or appearance of human hand does not lead to improved neural connectivity to the sensorimotor system of amputees. The functions of modern prosthetic hands do not match the dexterity of human hand due primarily to lack of sensory awareness and compliant actuation. Lately, progress in restoring sensory feedback has marked a significant step forward in improving neural continuity of sensory information from prosthetic hands to amputees. However, little effort has been made to replicate the compliant property of biological muscle when actuating prosthetic hands. Furthermore, a full-fledged biorealistic approach to designing prosthetic hands has not been contemplated in neuroprosthetic research. In this perspective article, we advance a novel view that a prosthetic hand can be integrated harmoniously with amputees only if neural compatibility to the sensorimotor system is achieved. Our ongoing research supports that the next-generation prosthetic hand must incorporate biologically realistic actuation, sensing, and reflex functions in order to fully attain neural compatibility.

scholarly journals Control of prosthetic hand by using mechanomyography signals based on support-vector machine classifier

2021 ◽  
Vol 23 (2) ◽  
pp. 1180
Author(s):  
Firas Saaduldeen Ahmed ◽  
Noha Abed-Al-Bary Al-jawady
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Wavelet Packet ◽  
Support Vector ◽  
Prosthetic Hand ◽  
Hand Gestures ◽  
Prosthetic Devices ◽  
Prosthetic Hands ◽  
Average Accuracy ◽  
And Control

<div>Prosthetic devices are necessary to help amputees achieve their daily activity in the natural way possible. The prosthetic hand has controlled by type of signals such as electromyography (EMG) and mechanomyography (MMG). The MMG signals have represented mechanical signals that generate during muscle contraction. These signals can be detected by accelerometers or microphones and any kind of sensors that can detect muscle vibrations. The contribution of the current paper is classifying hand gestures and control prosthetic hands depends on pattern recognition through accelerometer and microphone are to detect MMG signals. In addition to the cost of prosthetic hand less than other designs. Six subjects are involved. In this present work is the devices. In this study, two of them are amputee subjects. Each subject performs seven classes of movements. Pattern recognition (PR) is used to classify hand gestures. The wavelet packet transform (WPT) and root mean square (RMS) as features extracted from the signals and support vector machine (SVM) as a classifier. The average accuracy is 88.94% for offline tests and 84.45% for online tests. 3D printing technology is used in this study to build prosthetic hands.</div>

On the design of robotic hands for brain–machine interface

2006 ◽  
Vol 20 (5) ◽  
pp. 1-9 ◽  
Author(s):  
Yoky Matsuoka ◽  
Pedram Afshar ◽  
Michael Oh
Keyword(s):  
Brain Machine Interface ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Robotic Hand ◽  
Neural Signals ◽  
Robotic Hands ◽  
Intimate Contact ◽  
Machine Interface ◽  
Optimal Approach ◽  
And Control

✓ Brain–machine interface (BMI) is the latest solution to a lack of control for paralyzed or prosthetic limbs. In this paper the authors focus on the design of anatomical robotic hands that use BMI as a critical intervention in restorative neurosurgery and they justify the requirement for lower-level neuromusculoskeletal details (relating to biomechanics, muscles, peripheral nerves, and some aspects of the spinal cord) in both mechanical and control systems. A person uses his or her hands for intimate contact and dexterous interactions with objects that require the user to control not only the finger endpoint locations but also the forces and the stiffness of the fingers. To recreate all of these human properties in a robotic hand, the most direct and perhaps the optimal approach is to duplicate the anatomical musculoskeletal structure. When a prosthetic hand is anatomically correct, the input to the device can come from the same neural signals that used to arrive at the muscles in the original hand. The more similar the mechanical structure of a prosthetic hand is to a human hand, the less learning time is required for the user to recreate dexterous behavior. In addition, removing some of the nonlinearity from the relationship between the cortical signals and the finger movements into the peripheral controls and hardware vastly simplifies the needed BMI algorithms. (Nonlinearity refers to a system of equations in which effects are not proportional to their causes. Such a system could be difficult or impossible to model.) Finally, if a prosthetic hand can be built so that it is anatomically correct, subcomponents could be integrated back into remaining portions of the user's hand at any transitional locations. In the near future, anatomically correct prosthetic hands could be used in restorative neurosurgery to satisfy the user's needs for both aesthetics and ease of control while also providing the highest possible degree of dexterity.

Study on Fuzzy Multiple Reference Model Adaptive Control Strategies

2014 ◽  
Vol 962-965 ◽  
pp. 2932-2938
Author(s):  
Shu Xing Peng ◽  
Hua Xue ◽  
Dong Dong Li
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Reference Model ◽  
Control Strategies ◽  
Dynamic Performance ◽  
Control Precision ◽  
High Dynamic ◽  
Suitable Reference ◽  
And Control ◽  
Multiple Reference

A new fuzzy multiple reference model adaptive control method combined with fuzzy select and conventional adaptive control is presented. To overcome the control difficulties which due to significant and unpredictable system parameter variations, fuzzy logic rules are designed to choose the suitable reference model. The new method is applied to control the speed servo system of dynamic model of BLDCM, and the simulation results show it works well with high dynamic performance and control precision under the condition of great change in reference speed and load torque.

The compliance of robotic hands – from functionality to mechanism

2015 ◽  
Vol 35 (3) ◽  
pp. 281-286 ◽  
Author(s):  
Rui Li ◽  
Wei Wu ◽  
Hong Qiao
Keyword(s):  
Nervous System ◽  
Control Mechanism ◽  
Control Method ◽  
Physical Structure ◽  
Robotic Assembly ◽  
Automation System ◽  
Content Type ◽  
Assembly Automation ◽  
Human Motor ◽  
And Control

Purpose – The purpose of this paper is to introduce the physical structure and the control mechanism of human motor nervous system to the robotic system in a tentative manner to improve the compliance/flexibility/versatility of the robot. Design/methodology/approach – A brief review is focused on the concept of compliance, the compliance-based methods and the application of some compliance-based devices. Combined with the research on the physical structure and the control mechanism of human motor nervous system, a new drive structure and control method is proposed. Findings – Introducing the physical structure and the control mechanism of human motor nervous system can improve the compliance/flexibility/versatility of the robot, without bringing in more complexity or inefficiency to the system, which helps in the assembly automation tasks. Originality/value – The proposed drive structure and control method are useful to build up a novel, low-cost robotic assembly automation system, which is easy to interact and cooperate with humans.

scholarly journals Review of Recent Progress in Robotic Knee Prosthesis Related Techniques: Structure, Actuation and Control

2021 ◽  
Vol 18 (4) ◽  
pp. 764-785
Author(s):  
Yuanxi Sun ◽  
Hao Tang ◽  
Yuntao Tang ◽  
Jia Zheng ◽  
Dianbiao Dong ◽  
...  
Keyword(s):  
Recent Progress ◽  
Mechanical Performance ◽  
Control Method ◽  
Mechanical Design ◽  
Knee Prosthesis ◽  
Control Strategies ◽  
Knee Prostheses ◽  
Mode Identification ◽  
Gait Adaptation ◽  
Saving Effect

AbstractAs the essential technology of human-robotics interactive wearable devices, the robotic knee prosthesis can provide above-knee amputations with functional knee compensations to realize their physical and psychological social regression. With the development of mechanical and mechatronic science and technology, the fully active knee prosthesis that can provide subjects with actuating torques has demonstrated a better wearing performance in slope walking and stair ascent when compared with the passive and the semi-active ones. Additionally, with intelligent human-robotics control strategies and algorithms, the wearing effect of the knee prosthesis has been greatly enhanced in terms of stance stability and swing mobility. Therefore, to help readers to obtain an overview of recent progress in robotic knee prosthesis, this paper systematically categorized knee prostheses according to their integrated functions and introduced related research in the past ten years (2010–2020) regarding (1) mechanical design, including uniaxial, four-bar, and multi-bar knee structures, (2) actuating technology, including rigid and elastic actuation, and (3) control method, including mode identification, motion prediction, and automatic control. Quantitative and qualitative analysis and comparison of robotic knee prosthesis-related techniques are conducted. The development trends are concluded as follows: (1) bionic and lightweight structures with better mechanical performance, (2) bionic elastic actuation with energy-saving effect, (3) artificial intelligence-based bionic prosthetic control. Besides, challenges and innovative insights of customized lightweight bionic knee joint structure, highly efficient compact bionic actuation, and personalized daily multi-mode gait adaptation are also discussed in-depth to facilitate the future development of the robotic knee prosthesis.

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