Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics
The human hand is a unique and highly complex effector. The ability to describe hand kinematics with a small number of features suggests that complex hand movements are composed of combinations of simpler movements. This would greatly simplify the neural control of hand movements. If such movement p...
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| Format: | Journal article |
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Cell Press
2022
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| _version_ | 1826309067798216704 |
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| author | Keogh, C Fitzgerald, J |
| author_facet | Keogh, C Fitzgerald, J |
| author_sort | Keogh, C |
| collection | OXFORD |
| description | The human hand is a unique and highly complex effector. The ability to describe hand kinematics with a small number of features suggests that complex hand movements are composed of combinations of simpler movements. This would greatly simplify the neural control of hand movements. If such movement primitives exist, a dimensionality reduction approach designed to exploit these features should outperform existing methods. We developed a deep neural network to capture the temporal dynamics of movements and demonstrate that the features learned allow accurate representation of functional hand movements using lower-dimensional representations than previously reported. We show that these temporal features are highly conserved across individuals and can interpolate previously unseen movements, indicating that they capture the intrinsic structure of hand movements. These results indicate that functional hand movements are defined by a low-dimensional basis set of movement primitives with important temporal dynamics and that these features are common across individuals. |
| first_indexed | 2024-03-07T07:28:43Z |
| format | Journal article |
| id | oxford-uuid:568eaa40-37e0-4612-8bbf-efdc9e25b34f |
| institution | University of Oxford |
| last_indexed | 2024-03-07T07:28:43Z |
| publishDate | 2022 |
| publisher | Cell Press |
| record_format | dspace |
| spelling | oxford-uuid:568eaa40-37e0-4612-8bbf-efdc9e25b34f2023-01-04T14:15:45ZDecomposition into dynamic features reveals a conserved temporal structure in hand kinematicsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:568eaa40-37e0-4612-8bbf-efdc9e25b34fSensory neuroscienceBiological sciencesNeuroscienceSymplectic ElementsCell Press2022Keogh, CFitzgerald, JThe human hand is a unique and highly complex effector. The ability to describe hand kinematics with a small number of features suggests that complex hand movements are composed of combinations of simpler movements. This would greatly simplify the neural control of hand movements. If such movement primitives exist, a dimensionality reduction approach designed to exploit these features should outperform existing methods. We developed a deep neural network to capture the temporal dynamics of movements and demonstrate that the features learned allow accurate representation of functional hand movements using lower-dimensional representations than previously reported. We show that these temporal features are highly conserved across individuals and can interpolate previously unseen movements, indicating that they capture the intrinsic structure of hand movements. These results indicate that functional hand movements are defined by a low-dimensional basis set of movement primitives with important temporal dynamics and that these features are common across individuals. |
| spellingShingle | Sensory neuroscience Biological sciences Neuroscience Keogh, C Fitzgerald, J Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics |
| title | Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics |
| title_full | Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics |
| title_fullStr | Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics |
| title_full_unstemmed | Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics |
| title_short | Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics |
| title_sort | decomposition into dynamic features reveals a conserved temporal structure in hand kinematics |
| topic | Sensory neuroscience Biological sciences Neuroscience |
| work_keys_str_mv | AT keoghc decompositionintodynamicfeaturesrevealsaconservedtemporalstructureinhandkinematics AT fitzgeraldj decompositionintodynamicfeaturesrevealsaconservedtemporalstructureinhandkinematics |