ArticleJournal of neuroengineering and rehabilitation2014
Analysis of hand synergies in healthy subjects during bimanual manipulation of various objects.
Article in Journal of neuroengineering and rehabilitation, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues.Sensors (Basel, Switzerland) · 2021Pooled it
- Exploring synergistic patterns in bimanual distal limb movements through low dimensional representations.Scientific reports · 2025Article
- Synergy-Based Evaluation of Hand Motor Function in Object Handling Using Virtual and Mixed Realities.Sensors (Basel, Switzerland) · 2025Article
- Task demands modulate distal limb handedness: A comparative analysis of prehensile synergies of the dominant and non-dominant hand.Scientific reports · 2024Article
- Article
- Comparison of synergy patterns between the right and left hand while performing postures and object grasps.Scientific reports · 2023Article
- The BE-UJI hand function activity set: a reduced set of activities for the evaluation of the healthy and pathological hand.Journal of neuroengineering and rehabilitation · 2023Article
- EMG space similarity feedback promotes learning of expert-like muscle activation patterns in a complex motor skill.Frontiers in human neuroscience · 2022Article
- A user-friendly automatic toolbox for hand kinematic analysis, clinical assessment and postural synergies extraction.Frontiers in bioengineering and biotechnology · 2022Article
- A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods.Frontiers in neurorobotics · 2022Review
- Synergy-Based Sensor Reduction for Recording the Whole Hand Kinematics.Sensors (Basel, Switzerland) · 2021Article
- Quantitative Investigation of Hand Grasp Functionality: Thumb Grasping Behavior Adapting to Different Object Shapes, Sizes, and Relative Positions.Applied bionics and biomechanics · 2021Article
- A Systematic Analysis of Hand Movement Functionality: Qualitative Classification and Quantitative Investigation of Hand Grasp Behavior.Frontiers in neurorobotics · 2021Article
- Learning Grasp Configuration Through Object-Specific Hand Primitives for Posture Planning of Anthropomorphic Hands.Frontiers in neurorobotics · 2021Article
- Quantitative Investigation of Hand Grasp Functionality: Hand Joint Motion Correlation, Independence, and Grasping Behavior.Applied bionics and biomechanics · 2021Article
- Sensing form - finger gaiting as key to tactile object exploration - a data glove analysis of a prototypical daily task.Journal of neuroengineering and rehabilitation · 2020Article
- Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset.Journal of neuroengineering and rehabilitation · 2019Article
- A Comprehensive Spatial Mapping of Muscle Synergies in Highly Variable Upper-Limb Movements of Healthy Subjects.Frontiers in physiology · 2019Article
- Muscle Synergy Analysis of a Hand-Grasp Dataset: A Limited Subset of Motor Modules May Underlie a Large Variety of Grasps.Frontiers in neurorobotics · 2018Article
- Analysis of Hand and Wrist Postural Synergies in Tolerance Grasping of Various Objects.PloS one · 2016Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundHand synergies have been extensively studied over the last few decades. Objectives of such research are numerous. In neuroscience, the aim is to improve the understanding of motor control and its ability to reduce the control dimensionality. In applied research fields like robotics the aim is to build biomimetic hand structures, or in prosthetics to design more performant underactuated replacement hands. Nevertheless, most of the synergy schemes identified to this day have been obtained from grasping experiments performed with one single (generally dominant) hand to objects placed in a given position and orientation in space. Aiming at identifying more generic synergies, we conducted similar experiments on postural synergy identification during bimanual manipulation of various objects in order to avoid the factors due to the extrinsic spatial position of the objects.
methodsTen healthy naive subjects were asked to perform a selected "grasp-give-receive" task with both hands using 9 objects. Subjects were wearing Cyberglove Ⓒ on both hands, allowing a measurement of the joint posture (15 degrees of freedom) of each hand. Postural synergies were then evaluated through Principal Component Analysis (PCA). Matches between the identified Principal Components and the human hand joints were analyzed thanks to the correlation matrix. Finally, statistical analysis was performed on the data in order to evaluate the effect of some specific variables on the hand synergies: object shape, hand side (i.e., laterality) and role (giving or receiving hand).
resultsResults on PCs are consistent with previous literature showing that a few principal components might be sufficient to describe a large variety of different grasps. Nevertheless some simple and strong correlations between PCs and clearly identified sets of hand joints were obtained in this study. In addition, these groupings of DoF corresponds to well-defined anatomo-functional finger joints according to muscle groups. Moreover, despite our protocol encouraging symmetric grasping, some right-left side differences were observed.
conclusionThe set of identified synergies presented here should be more representative of hand synergies in general since they are based on both hands motion. Preliminary results, that should be deepened, also highlight the influence of hand dominance and side. Thanks to their strong correlation with anatomo-functional joints, these synergies could therefore be used to design underactuated robotics hands.
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