Hello. My name is Jaime Maldonado. I'm a PhD\h student at the Cognitive Neuroinformatics group\h\h
at the University of Bremen. Together\h with my co-author Christoph Zetzsche\h\h
we investigated the differences between\h reach-to-grasp and reach-to-place movements\h\h
executed in virtual reality during the\h manipulation of different household objects.
In physical environments\h the sensorimotor features of\h\h
transport and place movements depend on the\h characteristics of the manipulated object.\h\h
Object characteristics like size and\h weight influence the movement kinematics.\h\h
Furthermore, different objects produce\h characteristic sensory feedback in the visual,\h\h
haptic and acoustic modalities. Previous research\h has shown that the kinematics of reach-to-grasp\h\h
movements are influenced by the object's\h intrinsic and extrinsic properties. Intrinsic\h\h
properties are the object's size, weight, and\h type. Extrinsic properties are the distance\h\h
and the placement of the object with respect to\h the subject. These properties have been shown\h\h
to influence the peak speed and the percentage\h of time to peak a speed of the reach movement.
Object manipulation in virtual and physical\h environments differ on the sensory information\h\h
perceived by the subject. In VR, without haptic\h or acoustic rendering only visual feedback is\h\h
produced during the manipulation of an object. For\h example, a contact event between the hands and an\h\h
object or between an object and a surface\h is perceived only in the visual modality.\h\h
Thus, under these sensory conditions motor control\h\h
during object manipulation depends\h on vision and proprioception.
In contrast to physical environments, the\h lack of acoustic and haptic rendering has\h\h
two implications: the movement to reach for an\h object and the movement to place a grasped object\h\h
are executed under the same physical conditions.\h For both movements subjects either move their\h\h
hands free in the space or hold controllers for\h interaction. Furthermore, the manipulation of\h\h
virtual objects produces a similar motor feedback\h regardless of the object type or its dimensions,\h\h
whereby the subject perceives the movement of the\h hand and the dynamics of the hand-held controller.
Considering these factors, the motor feedback\h is completely equivalent for a reach-to-grasp\h\h
and a reach-to-place action and it does\h not depend on the object. Given that\h\h
reach and place movements in VR are executed\h under the same physical conditions it could be\h\h
hypothesized that there are no differences between\h reach-to-grasp and reach-to-place movements.\h\h
Therefore, if a subject would act optimally\h with respect to the VR situation, it would\h\h
use a one-and-the-same velocity profile for the\h reach-to-grasp and for the reach-to-place actions\h\h
invariably for all objects. On the contrary,\h significant differences would provide evidence\h\h
that persons adjust motor output as a function\h of the manipulated object and the task.
In this paper we investigated the differences\h in the kinematic parameters of reach-to-grasp\h\h
and reach-to-place movements executed in VR and\h determined whether these differences depend on the\h\h
manipulated object. For this purpose we analyzed\h the manipulations contained in the HAVE dataset.\h\h
Subjects were given the simple instruction to set\h the table, for which they took objects out of a\h\h
cupboard and placed them on the table at the\h will. We chose this dataset due to the large\h\h
variability with respect to the objects used, the\h spatial configuration of the objects on the table,\h\h
the number of objects used, and the\h order in which objects were manipulated.
We analyzed reach-to-grasp and reach-to-place\h actions with different objects performed by\h\h
82 participants. In total we analyzed 837\h reach-to-grasp and 837 reach-to-place actions\h\h
performed with cups, cutlery, glasses,\h\h
plates and wine glasses. We only considered\h right hand manipulations for the analysis.
We identified the reach-to-grasp and\h the reach-to-place actions within\h\h
the hand trajectories available in the dataset.\h\h
Kinematic features were computed to compare\h the characteristics of both movements.\h\h
The peak speed, PS, provides an indirect measure\h of the speed-accuracy tradeoff associated with\h\h
movement, where lower speeds indicate a more\h accurate and careful behavior. Additionally we\h\h
measured the symmetry of the speed profile by\h computing the proportion of time to peak speed,\h\h
PTPS. PTPS values below 0.5 indicate\h a longer deceleration phase.
For peak speed we encountered significant\h main effects for action and a significant\h\h
interaction of action by object, with\h significant differences for the glass, plate,\h\h
and wineglass objects. This\h indicates that, for example,\h\h
persons were more careful during the\h placing movement of these objects.
For the proportion of time to peak a speed we\h encountered significant main effects for object.\h\h
Post-hoc pairwise comparisons reveal significant\h differences between the following object pairs:\h\h
cup-cutlery, cup-plate, and cup-wine glass.\h\h
On the object level all PTPS values were\h significantly lower than 0.5. This means\h\h
that both reach-to-grasp and reach-to-place\h movements had a longer deceleration phase.
We observed action-object dependencies in the\h PS and PTPS parameters of the speed profiles.\h\h
In particular, differences on the object\h level indicate that intrinsic object\h\h
properties modulated the motor behavior in VR.\h Our results show that unconstrained actions in\h\h
VR show characteristics which are also typical\h of actions executed in physical environments:\h\h
reach-to-grasp movements are faster than\h reach-to-place movements for some objects\h\h
and reach-to-grasp and reacht-to-place movements\h are asymmetric with longer deceleration phases.\h\h
Furthermore, the object-dependent\h differences suggest that cognitive\h\h
factors modulated motor output based\h on the object type and the action.
Our results are suitable of being applied\h in the design of object manipulation rules.\h\h
For example, measuring high speed combined\h with short acceleration time could trigger the\h\h
breaking of a glass. This would encourage a more\h careful behavior during subsequent manipulations.
Our results can also be applied for\h the simulation of object manipulations\h\h
performed by virtual agents. This would enable\h them to produce a more human-like behavior\h\h
and it could also improve the object-weight\h consistency for object manipulations.
Thanks for your attention!