Abstract
In this study, we designed a robot-based method to compute a mechanical impedance model that could extract the viscoelastic properties of the wrist joint. Thirteen subjects participated in the experiment, testing both dominant and nondominant hands. Specifically, the robotic device delivered position-controlled disturbances in the flexion-extension degree of freedom of the wrist. The external perturbations were characterized by small amplitudes and fast velocities, causing rotation at the wrist joint. The viscoelastic characteristics of the mechanical impedance of the joint were evaluated from the wrist kinematics and corresponding torques. Since the protocol used position inputs to determine changes in mean wrist torque, a detailed analysis of wrist joint dynamics could be made. The scientific question was whether and how these mechanical features changed with various grip demands and perturbation velocities. Nine experimental conditions were tested for each hand, given by the combination of three velocity perturbations (fast, medium, and slow) and three hand grip conditions [self-selected grip, medium and high grip force, as percentage of the maximum voluntary contraction (MVC)]. Throughout the experiments, electromyographic signals of the extensor carpi radialis (ECR) and the flexor carpi radialis (FCR) were recorded. The novelty of this work included a custom-made soft grip sensor, wrapped around the robotic handle, to accurately quantify the grip force exerted by the subjects during experimentation. Damping parameters were in the range of measurements from prior studies and consistent among the different experimental conditions. Stiffness was independent of both direction and velocity of perturbations and increased with increasing grip demand. Both damping and stiffness were not different between the dominant and nondominant hands. These results are crucial to improving our knowledge of the mechanical characteristics of the wrist, and how grip demands influence these properties. This study is the foundation for future work on how mechanical characteristics of the wrist are affected in pathological conditions.
Highlights
Upper extremity movements are crucial elements of daily life and our sensorimotor system is capable of coordinating complex movements with apparent ease
Once we verified that the experimental protocol did not induce measurable levels of fatigue and the subjects succeeded to keep an approximately consistent grip level during the experimental conditions, we could proceed with the analysis of the results, focused on the calculation of the viscoelastic parameters: such parameters of the wrist mechanical impedance, evaluated by the least square approximation (LSA) method for each experimental condition, are reported in the Tables 4, 5
The purpose of this work was twofold: (1) to build and validate a mathematical model that could evaluate the viscoelastic properties of both wrists in a healthy, right-handed population, using a robotic device designed for the biomechanical analysis and motor rehabilitation of the wrist; (2) to explore the relationship between viscoelastic parameters of the wrist joint, perturbation velocity and grip force
Summary
Upper extremity movements are crucial elements of daily life and our sensorimotor system is capable of coordinating complex movements with apparent ease. Formica and colleagues estimated passive stiffness of the wrist joint, i.e., resistance to stretching in the absence of muscle activity, confirming low muscle activity and balanced coactivation, supported by the provision of posture stability by the robot. In this study, they demonstrated anisotropy of passive stiffness, suggesting that one purpose of wrist motor control involves compensating for passive stiffness of the wrist by rotating the joint along the direction of least stiffness. The aim of our work was to implement a quantitative assessment for the resistance of the wrist joint to external perturbations, often referred to as mechanical impedance (Hogan, 1990) (i.e., the dynamical relationship between wrist kinematics and torque). This contrasts with other protocols that use a very slow perturbation which cannot exclude a neural component
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