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Discrete vibro-tactile feedback prevents object slippage in hand prostheses more intuitively than other modalities

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Author(s)
Aboseria, Mohamed
Clemente, Francesco
Engels, Leonard F.
Cipriani, Christian
Keywords
Electrodes
Electromyography
Force
Myoelectric prosthesis
prosthetic hand
Prosthetics
Robot sensing systems
sensory feedback
slip feedback
Task analysis
unexpected event
Neuroscience (all)
Biomedical Engineering
Computer Science Applications1707 Computer Vision and Pattern Recognition
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URI
http://hdl.handle.net/20.500.12424/2555218
Online Access
http://hdl.handle.net/11382/523612
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7333
Abstract
In the case of a hand amputation, the affected can use myoelectric prostheses to substitute the missing limb and regain motor functionality. Unfortunately, these prostheses do not restore sensory feedback, thus users are forced to rely on vision to avoid object slippage. This is cognitively taxing, as it requires continuous attention to the task. Thus, providing functionally effective sensory feedback is pivotal to reduce the occurrence of slip events and reduce the users’ cognitive burden. However, only a few studies investigated which kind of feedback is the most effective for this purpose, mostly using unrealistic experimental scenarios. Here we attempt a more realistic simulation of involuntary hand opening and subsequent recovery of a stable grasp of the slipping object using a robotic hand operated by the subjects through a standard myoelectric control interface. We compared three stimulation modalities (vision, continuous grip force feedback and discrete slip feedback) and found that the discrete feedback allowed subjects to have higher success rates (close to 100%) in terms of objects recovered from slippage, basically requiring no learning. These results suggest that this simple yet effective feedback can be used to reduce grasp failures in prosthetic users, increasing their confidence in the device.
Date
2018
Type
info:eu-repo/semantics/article
Identifier
oai:www.iris.sssup.it:11382/523612
http://hdl.handle.net/11382/523612
10.1109/TNSRE.2018.2851617
2-s2.0-85049326382
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7333
Copyright/License
info:eu-repo/semantics/openAccess
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