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Optical Myography: Detecting Finger Movements by Looking at the Forearm

Nissler, Christian and Mouriki, Nikoleta and Castellini, Claudio (2016) Optical Myography: Detecting Finger Movements by Looking at the Forearm. Frontiers in Neurorobotics. Frontiers Media S.A.. DOI: 10.3389/fnbot.2016.00003 ISSN 1662-5218

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Official URL: http://journal.frontiersin.org/article/10.3389/fnbot.2016.00003/full

Abstract

One of the crucial problems found in the scientific community of assistive/rehabilitation robotics nowadays is that of automatically detecting what a disabled subject (for instance, a hand amputee) wants to do, exactly when she wants to do it, and strictly for the time she wants to do it. This problem, commonly called “intent detection,” has traditionally been tackled using surface electromyography, a technique which suffers from a number of drawbacks, including the changes in the signal induced by sweat and muscle fatigue. With the advent of realistic, physically plausible augmented- and virtual-reality environments for rehabilitation, this approach does not suffice anymore. In this paper, we explore a novel method to solve the problem, which we call Optical Myography (OMG). The idea is to visually inspect the human forearm (or stump) to reconstruct what fingers are moving and to what extent. In a psychophysical experiment involving ten intact subjects, we used visual fiducial markers (AprilTags) and a standard web camera to visualize the deformations of the surface of the forearm, which then were mapped to the intended finger motions. As ground truth, a visual stimulus was used, avoiding the need for finger sensors (force/position sensors, datagloves, etc.). Two machine-learning approaches, a linear and a non-linear one, were comparatively tested in settings of increasing realism. The results indicate an average error in the range of 0.05–0.22 (root mean square error normalized over the signal range), in line with similar results obtained with more mature techniques such as electromyography. If further successfully tested in the large, this approach could lead to vision-based intent detection of amputees, with the main application of letting such disabled persons dexterously and reliably interact in an augmented-/virtual-reality setup.

Item URL in elib:https://elib.dlr.de/105316/
Document Type:Article
Title:Optical Myography: Detecting Finger Movements by Looking at the Forearm
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Nissler, ChristianChristian.Nissler (at) dlr.dehttps://orcid.org/0000-0003-4361-9041
Mouriki, Nikoletanikoleta.mouriki (at) gmail.comUNSPECIFIED
Castellini, ClaudioClaudio.Castellini (at) dlr.dehttps://orcid.org/0000-0002-7346-2180
Date:11 April 2016
Journal or Publication Title:Frontiers in Neurorobotics
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.3389/fnbot.2016.00003
Editors:
EditorsEmail
Zacksenhouse, Miriammermz@technion.ac.il
Publisher:Frontiers Media S.A.
ISSN:1662-5218
Status:Published
Keywords:rehabilitation robotics, human–machine interface, hand prostheses, computer vision, myography
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Institute of Robotics and Mechatronics (since 2013) > Cognitive Robotics
Deposited By: Nissler, Christian
Deposited On:19 Jul 2016 09:21
Last Modified:08 Mar 2018 18:32

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