Electromyographic mapping of finger stiffness in tripod grasp: a proof of concept

Abstract

Effective execution of a manipulation task using prosthetic or robotic hands requires that the motion and the impedance profiles of the fingers be appropriately commanded. This, however, brings some design and control challenges regarding the individual planning and realization of the finger motion and stiffness trajectories. It appears that the central nervous system solves for this complexity in an effective and coordinated manner which has been well-recognized under the concept of hand synergies. While the exploitation of this concept in kinematic coordinates has lead to the development of several successful robotic designs and control strategies, its extension to dynamic coordinates, such as coordinated stiffening of the fingers, remains to be investigated. Indeed, in this study we provide preliminary evidence on the existence of such coordinated stiffening patterns in human fingers and establish initial steps towards a real-time and effective modelling of the finger stiffness in a tripod grasp. To achieve this goal, the endpoint stiffness of the thumb, index and middle fingers of five healthy subjects are experimentally identified and correlated with the electromyography (EMG) signals recorded from a dominant antagonistic pair of the forearm muscles. Our findings suggest that: i) the magnitude of the stiffness ellipses at the fingertips grows in a coordinated way, subsequent to the co-contraction of the forearm muscles; ii) the length of the ellipses' axes appears to have a nearly linear relationship with the co-contraction level of the antagonistic muscle pair

Grasp Stiffness Control in Robotic Hands Through Coordinated Optimization of Pose and Joint Stiffness

“…K q is based on the synergistic finger stiffness concept (CMS) and therefore mainly contributes to the modifications of the stiffness ellipsoid volume. For every finger f , this joint stiffness is a diagonal matrix K q,f = α Γ f , with K q,f ∈ R nq f ×nq f , where nq f is the number of joints of the finger, α is the common mode stiffness parameter (Nm/rad), and Γ f is a constant normalised vector implementing the coordinated stiffening of the hand fingers [13]. Therefore, the maximum achievable grasp stiffness volume is limited by the maximum applicable α.…”

“…Likewise, one of the first attempts to explore the concept of human hand synergies in the stiffness coordinates investigated the existence of a coordinated stiffening pattern in human fingertips during a tripod grasp [13]. In this study, the human fingertip stiffness profiles are estimated using external stochastic perturbations and illustrated by ellipsoids.…”

An instrumented manipulandum for human grasping studies

“…In [17] the device presented in this paper was profitably used in a new set up to study the strategies adopted by humans to modulate the stiffness at fingertips.…”

A Bio-inspired Grasp Stiffness Control for Robotic Hands

“…Similarly, regarding the stiffness control of the human hand, Milner and Franklin found that humans can modify the endpoint stiffness geometry by varying the fingers posture (CDS), being this process more energy-efficient than using muscle co-contraction (Milner and Franklin, 1998 ). In a complementary study, Rossi et al ( 2015 ), presented one of the first attempts to explore the concept of human hand synergies in the stiffness coordinates, showing the existence of a coordinated stiffening pattern in human fingertips during a tripod grasp (CMS). Authors estimated the human fingertip stiffness profiles using external stochastic perturbations, and illustrated them by ellipsoids.…”