Reach&Grasp: a multimodal dataset of the whole upper-limb during simple and complex movements
Abstract Upper-limb movement characterization is crucial for many applications, from research on motor control, to the extraction of relevant features for driving active prostheses. While this is usually performed using electrophysiological and/or kinematic measurements only, the collection of tacti...
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| Format: | Article |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04552-5 |
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| author | Dario Di Domenico Inna Forsiuk Simon Müller-Cleve Simone Tanzarella Florencia Garro Andrea Marinelli Michele Canepa Matteo Laffranchi Michela Chiappalone Chiara Bartolozzi Lorenzo De Michieli Nicolò Boccardo Marianna Semprini |
| author_facet | Dario Di Domenico Inna Forsiuk Simon Müller-Cleve Simone Tanzarella Florencia Garro Andrea Marinelli Michele Canepa Matteo Laffranchi Michela Chiappalone Chiara Bartolozzi Lorenzo De Michieli Nicolò Boccardo Marianna Semprini |
| author_sort | Dario Di Domenico |
| collection | DOAJ |
| description | Abstract Upper-limb movement characterization is crucial for many applications, from research on motor control, to the extraction of relevant features for driving active prostheses. While this is usually performed using electrophysiological and/or kinematic measurements only, the collection of tactile data during grasping movements could enrich the overall information about interaction with external environment. We provide a dataset collected from 10 healthy volunteers performing 16 tasks, including simple movements (i.e., hand opening/closing, wrist pronation/supination and flexion/extension, tridigital grasping, thumb abduction, cylindrical and spherical grasping) and more complex ones (i.e., reaching and grasping). The novelty consists in the inclusion of several types of recordings, namely electromyographic -both with bipolar and high-density configuration, kinematic-both with motion capture system and a sensorized glove, and tactile. The data is organized following the Brain Imaging Data Structure standard format and have been validated to ensure its reliability. It can be used to investigate upper-limb movements in physiological conditions, and to test sensor fusion approaches and control algorithms for prosthetics and robotic applications. |
| format | Article |
| id | doaj-art-109da1dc747541de803c0f21cc220baf |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-109da1dc747541de803c0f21cc220baf2025-02-09T12:11:31ZengNature PortfolioScientific Data2052-44632025-02-0112111310.1038/s41597-025-04552-5Reach&Grasp: a multimodal dataset of the whole upper-limb during simple and complex movementsDario Di Domenico0Inna Forsiuk1Simon Müller-Cleve2Simone Tanzarella3Florencia Garro4Andrea Marinelli5Michele Canepa6Matteo Laffranchi7Michela Chiappalone8Chiara Bartolozzi9Lorenzo De Michieli10Nicolò Boccardo11Marianna Semprini12Rehab Technologies Lab, Italian Institute of TechnologyRehab Technologies Lab, Italian Institute of TechnologyEvent-Driven Perception, Italian Institute of TechnologyEvent-Driven Perception, Italian Institute of TechnologyRehab Technologies Lab, Italian Institute of TechnologyRehab Technologies Lab, Italian Institute of TechnologyRehab Technologies Lab, Italian Institute of TechnologyRehab Technologies Lab, Italian Institute of TechnologyRehab Technologies Lab, Italian Institute of TechnologyEvent-Driven Perception, Italian Institute of TechnologyRehab Technologies Lab, Italian Institute of TechnologyRehab Technologies Lab, Italian Institute of TechnologyRehab Technologies Lab, Italian Institute of TechnologyAbstract Upper-limb movement characterization is crucial for many applications, from research on motor control, to the extraction of relevant features for driving active prostheses. While this is usually performed using electrophysiological and/or kinematic measurements only, the collection of tactile data during grasping movements could enrich the overall information about interaction with external environment. We provide a dataset collected from 10 healthy volunteers performing 16 tasks, including simple movements (i.e., hand opening/closing, wrist pronation/supination and flexion/extension, tridigital grasping, thumb abduction, cylindrical and spherical grasping) and more complex ones (i.e., reaching and grasping). The novelty consists in the inclusion of several types of recordings, namely electromyographic -both with bipolar and high-density configuration, kinematic-both with motion capture system and a sensorized glove, and tactile. The data is organized following the Brain Imaging Data Structure standard format and have been validated to ensure its reliability. It can be used to investigate upper-limb movements in physiological conditions, and to test sensor fusion approaches and control algorithms for prosthetics and robotic applications.https://doi.org/10.1038/s41597-025-04552-5 |
| spellingShingle | Dario Di Domenico Inna Forsiuk Simon Müller-Cleve Simone Tanzarella Florencia Garro Andrea Marinelli Michele Canepa Matteo Laffranchi Michela Chiappalone Chiara Bartolozzi Lorenzo De Michieli Nicolò Boccardo Marianna Semprini Reach&Grasp: a multimodal dataset of the whole upper-limb during simple and complex movements Scientific Data |
| title | Reach&Grasp: a multimodal dataset of the whole upper-limb during simple and complex movements |
| title_full | Reach&Grasp: a multimodal dataset of the whole upper-limb during simple and complex movements |
| title_fullStr | Reach&Grasp: a multimodal dataset of the whole upper-limb during simple and complex movements |
| title_full_unstemmed | Reach&Grasp: a multimodal dataset of the whole upper-limb during simple and complex movements |
| title_short | Reach&Grasp: a multimodal dataset of the whole upper-limb during simple and complex movements |
| title_sort | reach grasp a multimodal dataset of the whole upper limb during simple and complex movements |
| url | https://doi.org/10.1038/s41597-025-04552-5 |
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