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Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset
BACKGROUND: Hand grasp patterns require complex coordination. The reduction of the kinematic dimensionality is a key process to study the patterns underlying hand usage and grasping. It allows to define metrics for motor assessment and rehabilitation, to develop assistive devices and prosthesis cont...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540541/ https://www.ncbi.nlm.nih.gov/pubmed/31138257 http://dx.doi.org/10.1186/s12984-019-0536-6 |
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author | Jarque-Bou, Néstor J. Scano, Alessandro Atzori, Manfredo Müller, Henning |
author_facet | Jarque-Bou, Néstor J. Scano, Alessandro Atzori, Manfredo Müller, Henning |
author_sort | Jarque-Bou, Néstor J. |
collection | PubMed |
description | BACKGROUND: Hand grasp patterns require complex coordination. The reduction of the kinematic dimensionality is a key process to study the patterns underlying hand usage and grasping. It allows to define metrics for motor assessment and rehabilitation, to develop assistive devices and prosthesis control methods. Several studies were presented in this field but most of them targeted a limited number of subjects, they focused on postures rather than entire grasping movements and they did not perform separate analysis for the tasks and subjects, which can limit the impact on rehabilitation and assistive applications. This paper provides a comprehensive mapping of synergies from hand grasps targeting activities of daily living. It clarifies several current limits of the field and fosters the development of applications in rehabilitation and assistive robotics. METHODS: In this work, hand kinematic data of 77 subjects, performing up to 20 hand grasps, were acquired with a data glove (a 22-sensor CyberGlove II data glove) and analyzed. Principal Component Analysis (PCA) and hierarchical cluster analysis were used to extract and group kinematic synergies that summarize the coordination patterns available for hand grasps. RESULTS: Twelve synergies were found to account for > 80% of the overall variation. The first three synergies accounted for more than 50% of the total amount of variance and consisted of: the flexion and adduction of the Metacarpophalangeal joint (MCP) of fingers 3 to 5 (synergy #1), palmar arching and flexion of the wrist (synergy #2) and opposition of the thumb (synergy #3). Further synergies refine movements and have higher variability among subjects. CONCLUSION: Kinematic synergies are extracted from a large number of subjects (77) and grasps related to activities of daily living (20). The number of motor modules required to perform the motor tasks is higher than what previously described. Twelve synergies are responsible for most of the variation in hand grasping. The first three are used as primary synergies, while the remaining ones target finer movements (e.g. independence of thumb and index finger). The results generalize the description of hand kinematics, better clarifying several limits of the field and fostering the development of applications in rehabilitation and assistive robotics. |
format | Online Article Text |
id | pubmed-6540541 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65405412019-06-03 Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset Jarque-Bou, Néstor J. Scano, Alessandro Atzori, Manfredo Müller, Henning J Neuroeng Rehabil Research BACKGROUND: Hand grasp patterns require complex coordination. The reduction of the kinematic dimensionality is a key process to study the patterns underlying hand usage and grasping. It allows to define metrics for motor assessment and rehabilitation, to develop assistive devices and prosthesis control methods. Several studies were presented in this field but most of them targeted a limited number of subjects, they focused on postures rather than entire grasping movements and they did not perform separate analysis for the tasks and subjects, which can limit the impact on rehabilitation and assistive applications. This paper provides a comprehensive mapping of synergies from hand grasps targeting activities of daily living. It clarifies several current limits of the field and fosters the development of applications in rehabilitation and assistive robotics. METHODS: In this work, hand kinematic data of 77 subjects, performing up to 20 hand grasps, were acquired with a data glove (a 22-sensor CyberGlove II data glove) and analyzed. Principal Component Analysis (PCA) and hierarchical cluster analysis were used to extract and group kinematic synergies that summarize the coordination patterns available for hand grasps. RESULTS: Twelve synergies were found to account for > 80% of the overall variation. The first three synergies accounted for more than 50% of the total amount of variance and consisted of: the flexion and adduction of the Metacarpophalangeal joint (MCP) of fingers 3 to 5 (synergy #1), palmar arching and flexion of the wrist (synergy #2) and opposition of the thumb (synergy #3). Further synergies refine movements and have higher variability among subjects. CONCLUSION: Kinematic synergies are extracted from a large number of subjects (77) and grasps related to activities of daily living (20). The number of motor modules required to perform the motor tasks is higher than what previously described. Twelve synergies are responsible for most of the variation in hand grasping. The first three are used as primary synergies, while the remaining ones target finer movements (e.g. independence of thumb and index finger). The results generalize the description of hand kinematics, better clarifying several limits of the field and fostering the development of applications in rehabilitation and assistive robotics. BioMed Central 2019-05-28 /pmc/articles/PMC6540541/ /pubmed/31138257 http://dx.doi.org/10.1186/s12984-019-0536-6 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Jarque-Bou, Néstor J. Scano, Alessandro Atzori, Manfredo Müller, Henning Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset |
title | Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset |
title_full | Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset |
title_fullStr | Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset |
title_full_unstemmed | Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset |
title_short | Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset |
title_sort | kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540541/ https://www.ncbi.nlm.nih.gov/pubmed/31138257 http://dx.doi.org/10.1186/s12984-019-0536-6 |
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