Linear Integration of Tactile and Non-tactile Inputs Mediates Estimation of Fingertip Relative Position

The Effect of Kinesthetic and Artificial Tactile Noise and Variability on Stiffness Perception

Robot-assisted minimally invasive surgeries (RAMIS) have many benefits. A disadvantage, however, is the lack of haptic feedback. Haptic feedback is comprised of kinesthetic and tactile information, and we use both to form stiffness perception. Applying both kinesthetic and tactile feedback can enable more precise feedback than kinesthetic feedback alone. However, during remote surgeries, haptic noises and variations can be present. Therefore, toward designing haptic feedback for RAMIS, it is important to understand the effect of haptic manipulations on stiffness perception. We assessed the effect of two manipulations using stiffness discrimination tasks in which participants received force feedback and artificial skin stretch. In Experiment 1, we added sinusoidal noise to the artificial tactile signal, and found that the noise did not affect participants' stiffness perception or uncertainty. In Experiment 2, we varied either the kinesthetic or the artificial tactile information between consecutive interactions with an object. We found that the both forms of variability did not affect stiffness perception, but kinesthetic variability increased participants' uncertainty. We show that haptic feedback, comprised of force feedback and artificial skin stretch, provides robust haptic information even in the presence of noise and variability, and hence can potentially be both beneficial and viable in RAMIS.

Transfer and generalization of learned manipulation between unimanual and bimanual tasks

Successful object manipulation, such as preventing object roll, relies on the modulation of forces and centers of pressure (point of application of digits on each grasp surface) prior to lift onset to generate a compensatory torque. Whether or not generalization of learned manipulation can occur after adding or removing effectors is not known. We examined this by recruiting participants to perform lifts in unimanual and bimanual grasps and analyzed results before and after transfer. Our results show partial generalization of learned manipulation occurred when switching from a (1) unimanual to bimanual grasp regardless of object center of mass, and (2) bimanual to unimanual grasp when the center of mass was on the thumb side. Partial generalization was driven by the modulation of effectors' center of pressure, in the appropriate direction but of insufficient magnitude, while load forces did not contribute to torque generation after transfer. In addition, we show that the combination of effector forces and centers of pressure in the generation of compensatory torque differ between unimanual and bimanual grasping. These findings highlight that (1) high-level representations of learned manipulation enable only partial learning transfer when adding or removing effectors, and (2) such partial generalization is mainly driven by modulation of effectors' center of pressure.

A Novel Grip Force Measurement Concept for Tactile Stimulation Mechanisms - Design, Validation, and User Study

In this article, we developed a new grip force measurement concept that allows for embedding tactile stimulation mechanisms in a gripper. This concept is based on a single force sensor to measure the force applied on each side of the gripper, and substantially reduces tactor motion artifacts on force measurement. To test the feasibility of this new concept, we built a device that measures control of grip force in response to a tactile stimulation from a moving tactor. We calibrated and validated our device with a testing setup with a second force sensor over a range of 0 to 20 N without movement of the tactors. We tested the effect of tactor movement on the measured grip force, and measured artifacts of 1% of the measured force. We demonstrated that during the application of dynamically changing grip forces, the average errors were 2.9% and 3.7% for the left and right sides of the gripper, respectively. We characterized the bandwidth, backlash, and noise of our tactile stimulation mechanism. Finally, we conducted a user study and found that in response to tactor movement, participants increased their grip force, the increase was larger for a smaller target force, and depended on the amount of tactile stimulation.

A review of the neurobiomechanical processes underlying secure gripping in object manipulation

O'SHEA, H. and S. J. Redmond. A review of the neurobiomechanical processes underlying secure gripping in object manipulation. NEUROSCI BIOBEHAV REV 286-300, 2021. Humans display skilful control over the objects they manipulate, so much so that biomimetic systems have yet to emulate this remarkable behaviour. Two key control processes are assumed to facilitate such dexterity: predictive cognitive-motor processes that guide manipulation procedures by anticipating action outcomes; and reactive sensorimotor processes that provide important error-based information for movement adaptation. Notwithstanding increased interdisciplinary research interest in object manipulation behaviour, the complexity of the perceptual-sensorimotor-cognitive processes involved and the theoretical divide regarding the fundamentality of control mean that the essential mechanisms underlying manipulative action remain undetermined. In this paper, following a detailed discussion of the theoretical and empirical bases for understanding human dexterous movement, we emphasise the role of tactile-related sensory events in secure object handling, and consider the contribution of certain biophysical and biomechanical phenomena. We aim to provide an integrated account of the current state-of-art in skilled human-object interaction that bridges the literature in neuroscience, cognitive psychology, and biophysics. We also propose novel directions for future research exploration in this area.

Stability of perception of the hand's aperture in a grasp

KEY POINTS

How we judge the location of our body parts can be affected by a range of factors that change how our brain interprets proprioceptive signals. We examined the effect of several such factors on how we perceive an object's width and the spacing between our thumb and fingers when grasping. Grasp-related perceptions were slightly wider when using all digits, in line with our tendency to grasp larger objects with the entire hand. Surprisingly, these perceptions were not affected by the frames of reference for judgements (object width versus grasp aperture), whether the object was grasped actively or passively, or the strength of the grasp. These results show that the brain maintains a largely stable representation of the hand when grasping stationary objects. This stability may underpin our dexterity when grasping a vast array of objects.

ABSTRACT

Various factors can alter how the brain interprets proprioceptive signals, leading to errors in how we perceive our body and execute motor tasks. This study determined the effect of critical factors on hand-based perceptions. In Experiment 1, 20 participants grasped without lifting an unseen 6.5 cm-wide object with two grasp configurations: thumb and all fingers, and thumb and index finger. Participants reported perceived grasp aperture (body reference frame) or perceived object width (external reference frame) using visual charts. In Experiment 2, 20 participants grasped the object with three grasp intensities (1, 5 and 15% maximal grasp force) actively or passively and reported perceived grasp aperture. A follow-up experiment addressed whether results from Experiment 2 were influenced by the external force applied during passive grasp. Overall, there was a mean difference of 0.38 cm (95% confidence interval (CI), 0.12 to 0.63) between the two grasp configurations (all digits compared to thumb and index finger). Perceived object width compared to perceived grasp aperture differed by only -0.04 cm (95% CI, -0.30 to 0.21). There was no real effect of grasp intensity on perceived grasp aperture (-0.01 cm; 95% CI, -0.03 to 0.01) or grasp type (active versus passive; 0.18 cm; 95% CI, -0.19 to 0.55). Overall, grasp-related perceptions are slightly wider when using all digits, in line with our tendency to grasp larger objects with the entire hand. The other factors - frame of reference, grasp intensity and grasp type - had no meaningful effect on these perceptions. These results provide evidence that the brain maintains a largely stable representation of the hand.

Hand forces and placement are modulated and covary during anticipatory control of bimanual manipulation

Dexterous object manipulation relies on the feedforward and feedback control of kinetics (forces) and kinematics (hand shaping and digit placement). Lifting objects with an uneven mass distribution involves the generation of compensatory moments at object lift-off to counter object torques. This is accomplished through the modulation and covariation of digit forces and placement, which has been shown to be a general feature of unimanual manipulation. These feedforward anticipatory processes occur before performance-specific feedback. Whether this adaptation is a feature unique to unimanual dexterous manipulation or general across unimanual and bimanual manipulation is not known. We investigated the generation of compensatory moments through hand placement and force modulation during bimanual manipulation of an object with variable center of mass. Participants were instructed to prevent object roll during the lift. Similar to unimanual grasping, we found modulation and covariation of hand forces and placement for successful performance. Thus this motor adaptation of the anticipatory control of compensatory moment is a general feature across unimanual and bimanual effectors. Our results highlight the involvement of high-level representation of manipulation goals and underscore a sensorimotor circuitry for anticipatory control through a continuum of force and placement modulation of object manipulation across a range of effectors.

NEW & NOTEWORTHY This is the first study, to our knowledge, to show that successful bimanual manipulation of objects with asymmetrical centers of mass is performed through the modulation and covariation of hand forces and placements to generate compensatory moments. Digit force-to-placement modulation is thus a general phenomenon across multiple effectors, such as the fingers of one hand, and both hands. This adds to our understanding of integrating low-level internal representations of object properties into high-level task representations.