Résumé
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.