This paper presents the design and preliminary experimental verification of a multigrasp myoelectric controller. The controller enables the direct and proportional control of a multigrasp transradial prosthesis through a set of nine postures using two surface EMG electrodes. Five healthy subjects utilized the multigrasp controller to manipulate a virtual prosthesis to experimentally quantify the performance of the controller in terms of real time performance metrics. For comparison, the performance of the native hand was also characterized using a dataglove. The average overall transition times for the multigrasp myoelectric controller and the native hand with the dataglove were found to be 1.49 and 0.81 seconds, respectively. The transition rates for both were found to be the same (99.2%).
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http://dx.doi.org/10.1109/ICORR.2011.5975479 | DOI Listing |
Int J Med Robot
February 2024
State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin, China.
Background: Controlling a multi-grasp prosthetic hand still remains a challenge. This study explores the influence of merging gaze movements and augmented reality in bionics on improving prosthetic hand control.
Methods: A control system based on gaze movements, augmented reality, and myoelectric signals (i-MYO) was proposed.
IEEE Trans Neural Syst Rehabil Eng
February 2023
For transradial amputees, especially those with insufficient residual muscle activity, it is challenging to quickly obtain an appropriate grasping pattern for a multigrasp prosthesis. To address this problem, this study proposed a fingertip proximity sensor and a grasping pattern prediction method base on it. Rather than exclusively utilizing the EMG of the subject for the grasping pattern recognition, the proposed method used fingertip proximity sensing to predict the appropriate grasping pattern automatically.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
September 2018
Understanding the neurophysiological signals underlying voluntary motor control and decoding them for controlling limb prostheses is one of the major challenges in applied neuroscience and rehabilitation engineering. While pattern recognition of continuous myoelectric (EMG) signals is arguably the most investigated approach for hand prosthesis control, its underlying assumption is poorly supported, i.e.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
May 2017
The replacement of a missing hand by a prosthesis is one of the most fascinating challenges in rehabilitation engineering. State of art prostheses are curtailed by the physical features of the hand, like poor functionality and excessive weight. Here we present a new multi-grasp hand aimed at overcoming such limitations.
View Article and Find Full Text PDFPLoS One
April 2016
Department of Neurorehabilitation, University Medical Göttingen, Göttingen, Germany.
Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system's complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!