Shaping Human Movement via Bimanually-Dependent Haptic Force Feedback.

Haptic feedback can enhance training and performance of human operators; however, the design of haptic feedback for bimanual coordination in robot-assisted tasks (e.g., control of surgical robots) remains an open problem. In this study, we present four bimanually-dependent haptic force feedback conditions aimed at shaping bimanual movement according to geometric characteristics: the number of targets, direction, and symmetry. Haptic conditions include a virtual spring, damper, combination spring-damper, and dual springs placed between the hands. We evaluate the effects of these haptic conditions on trajectory shape, smoothness, and speed. We hypothesized that for subjects who perform worse with no haptic feedback (1) a spring will improve the shape of parallel trajectories, (2) a damper will improve the shape of point symmetric trajectories, (3) dual springs will improve the shape of trajectories with one target, and (4) a damper will improve smoothness for all trajectories. Hypotheses (1) and (2) were statistically supported at the < 0.001 level, but hypotheses (3) and (4) were not supported. Moreover, bimanually-dependent haptic feedback tended to improve shape accuracy for movements that subjects performed worse on under no haptic condition. Thus, bimanual haptic feedback based on geometric trajectory characteristics shows promise to improve performance in robot-assisted motor tasks.