A VR-Based Serious Game Associated to EMG Signal Processing and Sensory Feedback for Upper Limb Prosthesis Training

2021 ◽  
pp. 433-440
Author(s):  
Reidner Cavalcante ◽  
Aya Gaballa ◽  
John-John Cabibihan ◽  
Alcimar Soares ◽  
Edgard Lamounier
2013 ◽  
Vol 303-306 ◽  
pp. 261-265
Author(s):  
Peng Zhang ◽  
Qi Xu ◽  
Ji Ping He

An emerging challenge in developing intelligent prostheses is to replicate or recreate the sensory functions of natural limbs for amputees. Such functions mainly include tactile sensation and proprioception. This paper reviews the tactile receptors and proprioceptors in human upper limb, the artificial sensors in upper limb prosthesis, and the sensory feedback technology used for reconstruction of lost sensory function in the amputee’s upper limb.


2015 ◽  
Vol 1 (1) ◽  
pp. 484-487
Author(s):  
D. Hepp ◽  
J. Kirsch ◽  
F. Capanni

AbstractState of the art upper limb prostheses offer up to six active DoFs (degrees of freedom) and are controlled using different grip patterns. This low number of DoFs combined with a machine-human-interface which does not provide control over all DoFs separately result in a lack of usability for the patient. The aim of this novel upper limb prosthesis is both offering simplified control possibilities for changing grip patterns depending on the patients’ priorities and the improvement of grasp capability. Design development followed the design process requirements given by the European Medical Device Directive 93/42 ECC and was structured into the topics mechanics, software and drive technology. First user needs were identified by literature research and by patient feedback. Consequently, concepts were evaluated against technical and usability requirements. A first evaluation prototype with one active DoF per finger was manufactured. In a second step a test setup with two active DoF per finger was designed. The prototype is connected to an Android based smartphone application. Two main grip patterns can be preselected in the software application and afterwards changed and used by the EMG signal. Three different control algorithms can be selected: “all-day”, “fine” and “tired muscle”. Further parameters can be adjusted to customize the prosthesis to the patients’ needs. First patient feedback certified the prosthesis an improved level of handling compared to the existing devices. Using the two DoF test setup, the possibilities of finger control with a neural network are evaluated at the moment. In a first user feedback test, the smartphone based software application increased the device usability, e.g. the change within preselected grip patterns and the “tired muscle” algorithm. Although the overall software application was positively rated, the handling of the prosthesis itself needs to be proven within a patient study to be performed next. The capability of the neural network to control the hand has also to be proven in a next step.


2021 ◽  
Vol 10 (1) ◽  
pp. 48
Author(s):  
Ejay Nsugbe ◽  
Oluwarotimi William Samuel ◽  
Mojisola Grace Asogbon ◽  
Guanglin Li

The cybernetic interface within an upper-limb prosthesis facilitates a Human–Machine interaction and ultimately control of the prosthesis limb. A coherent flow between the phantom motion and subsequent actuation of the prosthesis limb to produce the desired gesture hinges heavily upon the physiological sensing source and its ability to acquire quality signals, alongside appropriate decoding of these intent signals with the aid of appropriate signal processing algorithms. In this paper, we discuss the sensing and signal processing aspects of the overall prosthesis control cybernetics, with emphasis on transradial, transhumeral, and shoulder disarticulate amputations, which represent considerable upper-limb amputees typically encountered within the population.


1998 ◽  
Vol 10 (4) ◽  
pp. 84-91 ◽  
Author(s):  
Peter J. Kyberd ◽  
David J. Beard ◽  
Jane J. Davey ◽  
J Dougall Morrison

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