scholarly journals Biorealistic Control of Hand Prosthesis Augments Functional Performance of Individuals With Amputation

2021 ◽  
Vol 15 ◽  
Author(s):  
Qi Luo ◽  
Chuanxin M. Niu ◽  
Chih-Hong Chou ◽  
Wenyuan Liang ◽  
Xiaoqian Deng ◽  
...  
Keyword(s):  
Functional Performance ◽  
Linear Feedback ◽  
Human Hand ◽  
Hand Prosthesis ◽  
Prosthetic Hands ◽  
Muscle Biomechanics ◽  
Reflex Control ◽  
Functional Gain ◽  
Real Time Computing ◽  
Stiffness Adjustment

The human hand has compliant properties arising from muscle biomechanics and neural reflexes, which are absent in conventional prosthetic hands. We recently proved the feasibility to restore neuromuscular reflex control (NRC) to prosthetic hands using real-time computing neuromorphic chips. Here we show that restored NRC augments the ability of individuals with forearm amputation to complete grasping tasks, including standard Box and Blocks Test (BBT), Golf Balls Test (GBT), and Potato Chips Test (PCT). The latter two were more challenging, but novel to prosthesis tests. Performance of a biorealistic controller (BC) with restored NRC was compared to that of a proportional linear feedback (PLF) controller. Eleven individuals with forearm amputation were divided into two groups: one with experience of myocontrol of a prosthetic hand and another without any. Controller performances were evaluated by success rate, failure (drop/break) rate in each grasping task. In controller property tests, biorealistic control achieved a better compliant property with a 23.2% wider range of stiffness adjustment than that of PLF control. In functional grasping tests, participants could control prosthetic hands more rapidly and steadily with neuromuscular reflex. For participants with myocontrol experience, biorealistic control yielded 20.4, 39.4, and 195.2% improvements in BBT, GBT, and PCT, respectively, compared to PLF control. Interestingly, greater improvements were achieved by participants without any myocontrol experience for BBT, GBT, and PCT at 27.4, 48.9, and 344.3%, respectively. The functional gain of biorealistic control over conventional control was more dramatic in more difficult grasp tasks of GBT and PCT, demonstrating the advantage of NRC. Results support the hypothesis that restoring neuromuscular reflex in hand prosthesis can improve neural motor compatibility to human sensorimotor system, hence enabling individuals with amputation to perform delicate grasps that are not tested with conventional prosthetic hands.

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.

scholarly journals Ten guidelines for the design of non-assembly mechanisms: The case of 3D-printed prosthetic hands

2018 ◽  
Vol 232 (9) ◽  
pp. 962-971 ◽  
Author(s):  
Juan Sebastian Cuellar ◽  
Gerwin Smit ◽  
Amir A Zadpoor ◽  
Paul Breedveld
Keyword(s):  
Developing Countries ◽  
3D Printing ◽  
Fused Deposition Modelling ◽  
Prosthetic Hand ◽  
Prosthetic Devices ◽  
Hand Prosthesis ◽  
Fused Deposition ◽  
Prosthetic Hands ◽  
Design Considerations ◽  
3D Printed

In developing countries, prosthetic workshops are limited, difficult to reach, or even non-existent. Especially, fabrication of active, multi-articulated, and personalized hand prosthetic devices is often seen as a time-consuming and demanding process. An active prosthetic hand made through the fused deposition modelling technology and fully assembled right after the end of the 3D printing process will increase accessibility of prosthetic devices by reducing or bypassing the current manufacturing and post-processing steps. In this study, an approach for producing active hand prosthesis that could be fabricated fully assembled by fused deposition modelling technology is developed. By presenting a successful case of non-assembly 3D printing, this article defines a list of design considerations that should be followed in order to achieve fully functional non-assembly devices. Ten design considerations for additive manufacturing of non-assembly mechanisms have been proposed and a design case has been successfully addressed resulting in a fully functional prosthetic hand. The hand prosthesis can be 3D printed with an inexpensive fused deposition modelling machine and is capable of performing different types of grasping. The activation force required to start a pinch grasp, the energy required for closing, and the overall mass are significantly lower than body-powered commercial prosthetic hands. The results suggest that this non-assembly design may be a good alternative for amputees in developing countries.

scholarly journals A novel energy-motion model for continuous sEMG decoding: from muscle energy to motor pattern

2020 ◽  
Author(s):  
Gang Liu ◽  
Lu Wang ◽  
Jing Wang
Keyword(s):  
Real Time ◽  
Degrees Of Freedom ◽  
Motor Pattern ◽  
Human Hand ◽  
Motion Model ◽  
Real Time Control ◽  
Prosthetic Hands ◽  
Time Control ◽  
Energy Mode ◽  
Muscle Energy

Myoelectric prosthetic hands create the possibility for amputees to control their prosthetics like native hands. However, user acceptance of the extant myoelectric prostheses is low. Unnatural control, lack of sufficient feedback, and insufficient functionality are cited as primary reasons. Recently, although many multiple degrees-of-freedom (DOF) prosthetic hands and tactile-sensitive electronic skins have been developed, no non-invasive myoelectric interfaces can decode both forces and motions for five-fingers independently and simultaneously. This paper proposes a myoelectric interface based on energy allocation and fictitious forces hypothesis by mimicking the natural neuromuscular system. The energy-based interface uses a kind of continuous “energy mode” in the level of the entire hand. According to tasks itself, each energy mode can adaptively and simultaneously implement multiple hand motions and exerting continuous forces for a single finger. Also, a few learned energy modes could extend to the unlearned energy mode, highlighting the extensibility of this interface. We evaluate the proposed system through off-line analysis and operational experiments performed on the expression of the unlearned hand motions, the amount of finger energy, and real-time control. With active exploration, the participant was proficient at exerting just enough energy to five fingers on “fragile” or “heavy” objects independently, proportionally, and simultaneously in real-time. The main contribution of this paper is proposing the bionic energy-motion model of hand: decoding a few muscle-energy modes of the human hand (only ten modes in this paper) map massive tasks of bionic hand.

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.

Hybrid Slip Detection Scheme Based Reflex Control of a Prosthetic Hand

2013 ◽  
Vol 433-435 ◽  
pp. 85-92
Author(s):  
Xue Feng Li ◽  
Xiao Gang Duan ◽  
Hua Deng
Keyword(s):  
Fuzzy Controller ◽  
Control Process ◽  
Prosthetic Hand ◽  
Force Estimation ◽  
Estimation Model ◽  
Detection Scheme ◽  
Prosthetic Hands ◽  
Accurate Judgment ◽  
Grasping Force ◽  
Reflex Control

Through the judgment of slip or not, human make proper adjustment to the grasping force and achieve stable manipulations. To reconstruct this function on a prosthetic hands platform, this paper presents a hybrid slip detect algorithm utilizing the PVDF sensor and FSR sensor. Then a reflex force estimation model is built to quantify the reflex force according to the intensity of slip in the reflex control process. Finally, through comparative experiments, the anti-jamming performance of the hybrid slip detect scheme is tested. A fuzzy controller is used to control the applied force and test the whole reflex control system. The results show that the hybrid slip detect scheme can make accurate judgment and has strong anti-jamming capacity; The output of the reflex force estimation model is accordance with the factual case; And as a whole, the grasping ability of prosthetic hand is substantially enhanced.

Dextrous Hands: Human, Prosthetic, and Robotic

1997 ◽  
Vol 6 (1) ◽  
pp. 29-56 ◽  
Author(s):  
Lynette Jones
Keyword(s):  
Sensory Processing ◽  
Human Hand ◽  
Robotic Hands ◽  
Prosthetic Hands ◽  
Robot Hands ◽  
Natural Sense ◽  
Manual Skills ◽  
Sense Of Touch ◽  
Manual Performance ◽  
Dextrous Hands

The sensory and motor capacities of the human hand are reviewed in the context of providing a set of performance characteristics against which prosthetic and dextrous robot hands can be evaluated. The sensors involved in processing tactile, thermal, and proprioceptive (force and movement) information are described, together with details on their spatial densities, sensitivity, and resolution. The wealth of data on the human hand's sensory capacities is not matched by an equivalent database on motor performance. Attempts at quantifying manual dexterity have met with formidable technological difficulties due to the conditions under which many highly trained manual skills are performed. Limitations in technology have affected not only the quantifying of human manual performance but also the development of prosthetic and robotic hands. Most prosthetic hands in use at present are simple grasping devices, and imparting a “natural” sense of touch to these hands remains a challenge. Several dextrous robot hands exist as research tools and even though some of these systems can outperform their human counterparts in the motor domain, they are still very limited as sensory processing systems. It is in this latter area that information from studies of human grasping and processing of object information may make the greatest contribution.

scholarly journals Surgically Implanted Electrodes Enable Real-Time Finger and Grasp Pattern Recognition for Prosthetic Hands

2020 ◽  
Author(s):  
A. K. Vaskov ◽  
P. P. Vu ◽  
N. North ◽  
A. J. Davis ◽  
T. A. Kung ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
High Speed ◽  
Degrees Of Freedom ◽  
Functional Performance ◽  
Recognition System ◽  
Sensory Nerves ◽  
Implanted Electrodes ◽  
Prosthetic Hands ◽  
Upper Limb Prosthesis

AbstractCurrently available prosthetic hands are capable of actuating anywhere from five to 30 degrees of freedom (DOF). However, grasp control of these devices remains unintuitive and cumbersome. To address this issue, we propose directly extracting finger commands from the neuromuscular system via electrodes implanted in residual innervated muscles and regenerative peripheral nerve interfaces (RPNIs). Two persons with transradial amputations had RPNIs created by suturing autologous free muscle grafts to their transected median, ulnar, and dorsal radial sensory nerves. Bipolar electrodes were surgically implanted into their ulnar and median RPNIs and into their residual innervated muscles. The implanted electrodes recorded local electromyography (EMG) with Signal-to-Noise Ratios ranging from 23 to 350 measured across various movements. In a series of single-day experiments, participants used a high speed pattern recognition system to control a virtual prosthetic hand in real-time. Both participants were able to transition between 10 pseudo-randomly cued individual finger and wrist postures in the virtual environment with an average online accuracy of 86.5% and latency of 255 ms. When the set was reduced to five grasp postures, average metrics improved to 97.9% online accuracy and 135 ms latency. Virtual task performance remained stable across untrained static arm positions while supporting the weight of the prosthesis. Participants also used the high speed classifier to switch between robotic prosthetic grips and complete a functional performance assessment. These results demonstrate that pattern recognition systems can use the high-quality EMG afforded by intramuscular electrodes and RPNIs to provide users with fast and accurate grasp control.SummarySurgically implanted electrodes recorded finger-specific electromyography enabling reliable finger and grasp control of an upper limb prosthesis.

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