Title
The contributions of skin stretch and kinesthetic information to static weight perception
Abstract
In this study, we examined the contributions of kinesthetic and skin stretch cues, in isolation and together, to the static perception of weight. In two psychophysical experiments, we asked participants either to detect on which hand a weight was presented or to compare between two weight cues. Two closed-loop controlled haptic devices were used to present weights with a precision of 0.05g to an end-effector held in a pinch grasp. Our results show that combining skin stretch and kinesthetic information leads to better weight detection thresholds than presenting uni-sensory cues does. For supra-threshold stimuli, Weber fractions ranged from 22-44%. Kinesthetic information was less reliable for lighter weights, while both sources of information were equally reliable for weights up to 300g. Our data for lighter weights complied with an Optimal Integration model, while for heavier weights, measurements were closer to predictions from a Sensory Capture model. The difference might be accounted for by the presence of correlated noise across the two cues with heavier weights, which would affect model predictions such that all our data could be explained through an Optimal Integration model. Our experiments provide device-independent measures that can be used to inform, for instance, skin stretch device design.
Year
DOI
Venue
2019
10.1109/WHC.2019.8816073
2019 IEEE World Haptics Conference (WHC)
Keywords
Field
DocType
skin stretch device design,kinesthetic information,static weight perception,kinesthetic skin,static perception,psychophysical experiments,weight cues,closed-loop controlled haptic devices,weight detection thresholds,uni-sensory cues,supra-threshold stimuli,optimal integration model,sensory capture model,mass 0.05 g,mass 300.0 g
Kinesthetic learning,Computer vision,GRASP,Computer science,Weight Perception,Artificial intelligence,Stimulus (physiology),Sensory system,Skin stretch,Perception,Haptic technology
Conference
ISBN
Citations 
PageRank 
978-1-5386-9462-6
0
0.34
References 
Authors
5
4
Name
Order
Citations
PageRank
Femke E. van Beek100.68
Raymond J. King201.69
Casey Brown310.69
Massimiliano Luca4779.90