Depth and direction effects in the prediction of static and shifted reaching goals from kinematics.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
12 08 2023
Historique:
received: 11 05 2023
accepted: 04 08 2023
medline: 16 8 2023
pubmed: 13 8 2023
entrez: 12 8 2023
Statut: epublish

Résumé

The kinematic parameters of reach-to-grasp movements are modulated by action intentions. However, when an unexpected change in visual target goal during reaching execution occurs, it is still unknown whether the action intention changes with target goal modification and which is the temporal structure of the target goal prediction. We recorded the kinematics of the pointing finger and wrist during the execution of reaching movements in 23 naïve volunteers where the targets could be located at different directions and depths with respect to the body. During the movement execution, the targets could remain static for the entire duration of movement or shifted, with different timings, to another position. We performed temporal decoding of the final goals and of the intermediate trajectory from the past kinematics exploiting a recurrent neural network. We observed a progressive increase of the classification performance from the onset to the end of movement in both horizontal and sagittal dimensions, as well as in decoding shifted targets. The classification accuracy in decoding horizontal targets was higher than the classification accuracy of sagittal targets. These results are useful for establishing how human and artificial agents could take advantage from the observed kinematics to optimize their cooperation in three-dimensional space.

Identifiants

pubmed: 37573413
doi: 10.1038/s41598-023-40127-3
pii: 10.1038/s41598-023-40127-3
pmc: PMC10423273
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

13115

Informations de copyright

© 2023. Springer Nature Limited.

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Auteurs

A Bosco (A)

Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy. annalisa.bosco2@unibo.it.
Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy. annalisa.bosco2@unibo.it.

M Filippini (M)

Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy.

D Borra (D)

Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy.

E A Kirchner (EA)

Department of Electrical Engineering and Information Technology, University of Duisburg-Essen, Duisburg, Germany.
Robotics Innovation Center, German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany.

P Fattori (P)

Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy.

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