Journal article Open Access
De Santis, Dalia; Mussa-Ivaldi, Ferdinando A.
<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>De Santis, Dalia</dc:creator> <dc:creator>Mussa-Ivaldi, Ferdinando A.</dc:creator> <dc:date>2020-05-11</dc:date> <dc:description>Background Body-machine interfaces map movements onto commands to external devices. Redundant motion signals derived from inertial sensors are mapped onto lower-dimensional device commands. Then, the device users face two problems, a) the structural problem of understanding the operation of the interface and b) the performance problem of controlling the external device with high efficiency. We hypothesize that these problems, while being distinct are connected in that aligning the space of body movements with the space encoded by the interface, i.e. solving the structural problem, facilitates redundancy resolution towards increasing efficiency, i.e. solving the performance problem. Methods Twenty unimpaired volunteers practiced controlling the movement of a computer cursor by moving their arms. Eight signals from four inertial sensors were mapped onto the two cursor’s coordinates on a screen. The mapping matrix was initialized by asking each user to perform free-form spontaneous upper-limb motions and deriving the two main principal components of the motion signals. Participants engaged in a reaching task for 18 min, followed by a tracking task. One group of 10 participants practiced with the same mapping throughout the experiment, while the other 10 with an adaptive mapping that was iteratively updated by recalculating the principal components based on ongoing movements. Results Participants quickly reduced reaching time while also learning to distribute most movement variance over two dimensions. Participants with the fixed mapping distributed movement variance over a subspace that did not match the potent subspace defined by the interface map. In contrast, participant with the adaptive map reduced the difference between the two subspaces, resulting in a smaller amount of arm motions distributed over the null space of the interface map. This, in turn, enhanced movement efficiency without impairing generalization from reaching to tracking. Conclusions Aligning the potent subspace encoded by the interface map to the user’s movement subspace guides redundancy resolution towards increasing movement efficiency, with implications for controlling assistive devices. In contrast, in the pursuit of rehabilitative goals, results would suggest that the interface must change to drive the statistics of user’s motions away from the established pattern and toward the engagement of movements to be recovered.</dc:description> <dc:identifier>https://zenodo.org/record/3823928</dc:identifier> <dc:identifier>10.1186/s12984-020-00681-7</dc:identifier> <dc:identifier>oai:zenodo.org:3823928</dc:identifier> <dc:relation>info:eu-repo/grantAgreement/EC/H2020/750464/</dc:relation> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:source>Journal of NeuroEngineering and Rehabilitation 17(61)</dc:source> <dc:subject>body-machine interface</dc:subject> <dc:subject>motor redundancy</dc:subject> <dc:subject>motor control</dc:subject> <dc:subject>rehabilitation</dc:subject> <dc:subject>human-machine interface</dc:subject> <dc:subject>coordination</dc:subject> <dc:subject>internal model</dc:subject> <dc:subject>co-adaptation</dc:subject> <dc:subject>iterative principal component analysis</dc:subject> <dc:title>Guiding functional reorganization of motor redundancy using a body-machine interface</dc:title> <dc:type>info:eu-repo/semantics/article</dc:type> <dc:type>publication-article</dc:type> </oai_dc:dc>