Dataset Information

Dataset for influence of visual and haptic feedback on the detection of threshold forces in a surgical grasping task.

ABSTRACT: The data is related to minimal force thresholds perception in robotic surgical grasping applications. The experimental setup included an indenter-based haptic device acting on the fingertip of a participant and a visual system that displays grasping tasks by a surgical grasper. The experiments included the display of two presentations at different force levels (i.e., grasping and indentation) in three different modes, namely, visual-alone, haptic-alone, and bimodal (i.e., combined). For each mode, the participants were asked to identify which of the two presentations was higher. Each experiment was repeated till the termination conditions were met. Sixty participants took part in these experiments. The experiments were randomized and the threshold forces were calculated based on an algorthim. The datasets contain the individual responses of each participant, the threshold forces calculations, and the number of iterations.

SUBMITTER: Cabibihan JJ

PROVIDER: S-EPMC8943423 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Dataset for influence of visual and haptic feedback on the detection of threshold forces in a surgical grasping task.

Cabibihan John-John JJ Alhaddad Ahmad Yaser AY Gulrez Tauseef T Yoon W Jong WJ

Data in brief 20220311

The data is related to minimal force thresholds perception in robotic surgical grasping applications. The experimental setup included an indenter-based haptic device acting on the fingertip of a participant and a visual system that displays grasping tasks by a surgical grasper. The experiments included the display of two presentations at different force levels (i.e., grasping and indentation) in three different modes, namely, visual-alone, haptic-alone, and bimodal (i.e., combined). For each mod ...[more]

PMID: 35341034

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Project description:How humans visually select where to grasp objects is determined by the physical object properties (e.g., size, shape, weight), the degrees of freedom of the arm and hand, as well as the task to be performed. We recently demonstrated that human grasps are near-optimal with respect to a weighted combination of different cost functions that make grasps uncomfortable, unstable, or impossible, e.g., due to unnatural grasp apertures or large torques. Here, we ask whether humans can consciously access these rules. We test if humans can explicitly judge grasp quality derived from rules regarding grasp size, orientation, torque, and visibility. More specifically, we test if grasp quality can be inferred (i) by using visual cues and motor imagery alone, (ii) from watching grasps executed by others, and (iii) through performing grasps, i.e., receiving visual, proprioceptive and haptic feedback. Stimuli were novel objects made of 10 cubes of brass and wood (side length 2.5 cm) in various configurations. On each object, one near-optimal and one sub-optimal grasp were selected based on one cost function (e.g., torque), while the other constraints (grasp size, orientation, and visibility) were kept approximately constant or counterbalanced. Participants were visually cued to the location of the selected grasps on each object and verbally reported which of the two grasps was best. Across three experiments, participants were required to either (i) passively view the static objects and imagine executing the two competing grasps, (ii) passively view videos of other participants grasping the objects, or (iii) actively grasp the objects themselves. Our results show that, for a majority of tested objects, participants could already judge grasp optimality from simply viewing the objects and imagining to grasp them, but were significantly better in the video and grasping session. These findings suggest that humans can determine grasp quality even without performing the grasp-perhaps through motor imagery-and can further refine their understanding of how to correctly grasp an object through sensorimotor feedback but also by passively viewing others grasp objects.

Dataset Information

Dataset for influence of visual and haptic feedback on the detection of threshold forces in a surgical grasping task.

Publications

Dataset for influence of visual and haptic feedback on the detection of threshold forces in a surgical grasping task.

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