title: Multidigit force control during unconstrained grasping in response to object perturbations.
abstract: Because of the complex anatomy of the human hand, in the absence of external constraints, a large number of postures and force combinations can be used to attain a stable grasp. Motor synergies provide a viable strategy to solve this problem of motor redundancy. In this study, we exploited the technical advantages of an innovative sensorized object to study unconstrained hand grasping within the theoretical framework of motor synergies. Participants were required to grasp, lift, and hold the sensorized object. During the holding phase, we repetitively applied external disturbance forces and torques and recorded the spatiotemporal distribution of grip forces produced by each digit. We found that the time to reach the maximum grip force during each perturbation was roughly equal across fingers, consistent with a synchronous, synergistic stiffening across digits. We further evaluated this hypothesis by comparing the force distribution of human grasping vs. robotic grasping, where the control strategy was set by the experimenter. We controlled the global hand stiffness of the robotic hand and found that this control algorithm produced a force pattern qualitatively similar to human grasping performance. Our results suggest that the nervous system uses a default whole hand synergistic control to maintain a stable grasp regardless of the number of digits involved in the task, their position on the objects, and the type and frequency of external perturbations.NEW & NOTEWORTHYWe studied hand grasping using a sensorized object allowing unconstrained finger placement. During object perturbation, the time to reach the peak force was roughly equal across fingers, consistently with a synergistic stiffening across fingers. Force distribution of a robotic grasping hand, where the control algorithm is based on global hand stiffness, was qualitatively similar to human grasping. This suggests that the central nervous system uses a default whole hand synergistic control to maintain a stable grasp.
authors.affiliation: Neuroinformatics Group, Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany; abdeldjallil.naceri@uni-bielefeld.de.
authors.forename: Abdeldjallil
authors.identifier: 0000-0002-0792-6258
authors.initials: A
authors.lastname: Naceri
authors.affiliation: Department of Systems Medicine and Centre of Space Bio-medicine, University of Rome "Tor Vergata," Rome, Italy.|Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy.
authors.forename: Alessandro
authors.identifier: 0000-0001-6269-4536
authors.initials: A
authors.lastname: Moscatelli
authors.affiliation: Neuroinformatics Group, Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany.
authors.forename: Robert
authors.initials: R
authors.lastname: Haschke
authors.affiliation: Neuroinformatics Group, Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany.
authors.forename: Helge
authors.initials: H
authors.lastname: Ritter
authors.affiliation: School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona; and.
authors.forename: Marco
authors.initials: M
authors.lastname: Santello
authors.affiliation: Faculty for Computer Science, Engineering, and Psychology, Ulm University, Ulm, Germany.
authors.forename: Marc O
authors.initials: MO
authors.lastname: Ernst
mesh_terms: D000328:Adult; D001696:Biomechanical Phenomena; D005260:Female; D005385:Fingers / Q000294:innervation / Q000502:physiology*; D018737:Hand Strength; D006801:Humans; D008297:Male; D009048:Motor Skills; D012371:Robotics / Q000295:instrumentation / Q000379:methods
keywords: control; force; grasping; manipulation; unconstrained