Discriminating Between Intensities and Velocities of Mid-Air Haptic Patterns

This study investigates people’s ability in discriminating between different intensities and velocities of mid-air haptic (MAH) sensations. Apart from estimating the just noticeable differences (JND), we also investigated the impact of age on discrimination performance, and the relationship between someone’s confidence in his/her performance and the actual discrimination performance. In an experimental set-up, involving 50 participants, we obtained a JND of 12.12% for intensities and 0.51 rev/s for velocities. Surprisingly, the impact of age on discrimination performance was only small and almost negligible. Furthermore, participants’ subjective perception of their discrimination performance aligned well with their actual discrimination performance. These results are encouraging for the use of intensities and velocities as dimensions in the design of MAH patterns to convey information to the user.

[1]  G. Essick,et al.  Discrimination and scaling of velocity of stimulus motion across the skin. , 1988, Somatosensory & motor research.

[2]  Young J. Kim,et al.  Interactive generalized penetration depth computation for rigid and articulated models using object norm , 2014, ACM Trans. Graph..

[3]  Orestis Georgiou,et al.  User engagement for mid-air haptic interactions with digital signage , 2019, PerDis.

[4]  David Geerts,et al.  Invisible Touch: How Identifiable are Mid-Air Haptic Shapes? , 2019, CHI Extended Abstracts.

[5]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[6]  Alessandro Moscatelli,et al.  Modeling psychophysical data at the population-level: the generalized linear mixed model. , 2012, Journal of vision.

[7]  Hiroyuki Shinoda,et al.  Tactile Stimulation by Repetitive Lateral Movement of Midair Ultrasound Focus , 2020, IEEE Transactions on Haptics.

[8]  Sriram Subramanian,et al.  UltraHaptics: multi-point mid-air haptic feedback for touch surfaces , 2013, UIST.

[9]  Ben Pelzer,et al.  influence.ME: Tools for Detecting Influential Data in Mixed Effects Models , 2012, R J..

[10]  Mohamad Eid,et al.  Haptogram: Ultrasonic Point-Cloud Tactile Stimulation , 2016, IEEE Access.

[11]  Sriram Subramanian,et al.  Perception of ultrasonic haptic feedback on the hand: localisation and apparent motion , 2014, CHI.

[12]  Chris J. Dallmann,et al.  The role of vibration in tactile speed perception. , 2015, Journal of neurophysiology.

[13]  E. Francisco,et al.  Vibrotactile amplitude discrimination capacity parallels magnitude changes in somatosensory cortex and follows Weber’s Law , 2008, Experimental Brain Research.

[14]  W. Simpson.  The method of constant stimuli is efficient , 1988, Perception & psychophysics.

[15]  David R. Large,et al.  Exploring the Use of Mid-Air Ultrasonic Feedback to Enhance Automotive User Interfaces , 2018, AutomotiveUI.

[16]  Orestis Georgiou,et al.  Laser Doppler Vibrometry and FEM Simulations of Ultrasonic Mid-Air Haptics , 2019, 2019 IEEE World Haptics Conference (WHC).

[17]  Vincent Hayward,et al.  Mechanical behavior of the fingertip in the range of frequencies and displacements relevant to touch. , 2012, Journal of biomechanics.

[18]  Lynette A. Jones,et al.  Application of Psychophysical Techniques to Haptic Research , 2013, IEEE Transactions on Haptics.

[19]  SubramanianSriram,et al.  Rendering volumetric haptic shapes in mid-air using ultrasound , 2014 .

[20]  J. Craig.  Difference threshold for intensity of tactile stimuli , 1972 .

[21]  Sriram Subramanian,et al.  Rendering volumetric haptic shapes in mid-air using ultrasound , 2014, ACM Trans. Graph..

[22]  Claudio Pacchierotti,et al.  Investigating the Recognition of Local Shapes Using Mid-air Ultrasound Haptics , 2019, 2019 IEEE World Haptics Conference (WHC).