Greetings everyone I am Ayush Bhardwaj today\h I will be presenting TangibleData: Interactive\h\h
Data Visualization with Mid-Air Haptics this\h work was done in collaboration with Junghoon Chae\h
Richard Noeske and professor Jin Ryong Kim from the\h Multimodal Interaction Lab at the University of\h\h
Texas at Dallas the amount and complexity of data\h is increasing each day 2d interfaces and visual\h\h
analytics cannot resolve these problems immersive\h analytics has emerged to tackle these issues\h\h
it uses 3d interfaces in augmented or virtual\h environments with multi-sensory feedback that\h\h
allows users to immerse themselves for\h better understanding of the data this\h\h
transition gave birth to opportunities like visual\h representation of 3D data natural interfaces for\h\h
3D data exploration but there are some underlying\h challenges as well 3d datasets consist of spatial\h\h
information like density and volume which is\h very difficult to perceive with visual cues only\h\h
3d datasets also suffer from issues like\h occlusion and distortion another challenge\h\h
is utilization of space it is very important to\h maintain the right size and location of datasets\h\h
Tangible Interaction using haptic feedback\h enhances immersion and provide additional\h\h
information using tactile channels this helps in\h understanding spatial features of complex datasets\h\h
haptic feedback is used in data visualization\h as kinesthetic and vibrotactile approaches\h\h
in kinesthetic approach force feedback is provided\h using phantom-omni arm but this technique has\h\h
limited degree of freedom in vibrotactile\h based approach feedback is provided using\h\h
handheld devices like VR controllers or tablets\h but this limits the tangible interaction\h\h
as the feedback is not provided on the bare\h hands due to this limitation it is difficult\h\h
to provide spatial information of datasets\h accurately this brings us to these questions\h\h
can we achieve a better understanding of data\h with tangible interaction in 3D immersive analytics\h\h
can we deliver spatial information like volume\h and density to the tactile channels to answer\h\h
these questions we need a bare hand haptic\h feedback which can be delivered in 3D space\h\h
this type of mid-air haptic feedback can be\h delivered using ultrasound haptic display it is\h\h
a non-contact-based haptic display which creates\h a focal point in mid-air using focused ultrasound\h\h
waves triggering at different time intervals this\h type of meta haptic feedback can really be useful\h\h
in immersive analytics as its intensity location\h and pattern can be controlled in real time\h\h
now I present to you Tangibledata. TangibleData\h is a toolkit that provides bare hand intuitive\h\h
tangible interface to explore 3D data sets using\h mid-air haptic tangible data uses oculus rift\h\h
as VR headset leap motion for hand tracking and\h stratos explore as ultrasound haptic display \hit
uses hand gestures and menu systems to provide\h an intuitive interface for data manipulation\h\h
it supports a pipeline which can create 3D\h scatterplots with haptic and visual cues\h\h
from a raw csv file in a csv file we can select\h the columns on which we want the haptics with any\h\h
intensity and any pattern we can also get our\h customized visual cues according to our needs\h\h
these values are then normalized and\h converted into 3D coordinates for unity 3D\h\h
TangibleData consists of two interaction modes\h first Direct Touch mode in which user can interact\h\h
and manipulate the datasets using his bare\h hands and feel the haptics at the same time\h\h
second Indirect Touch mode in which\h user has a plane attached at an offset\h\h
to exploit the sense of control for better\h interaction this mode provides flexibility\h\h
in terms of interaction with the datasets users\h can explore either a large or a small portion of the\h\h
dataset according to its needs by adjusting\h the size and offset of the plane from the hand
we implemented three data encoding techniques with\h haptics to provide complex and spatial information\h\h
about three data sets first density of clusters\h due to occlusion it's difficult to understand\h\h
the density of clusters in 3D scatterplots so we\h mapped individual data points as haptic sensation\h\h
sources with an intensity that is dependent\h on the density calculation, this serves as the\h\h
spatial information on the user's hand and helps\h user to understand the density of each cluster\h\h
second volume of tissues it's difficult to\h perceive volume of irregularly shaped tissues\h\h
in 3D space so we added simple\h shapes like tetrahedrons to calculate the volume\h\h
the computed volumes are then mapped as intensity\h levels for haptic feedback to provide volume\h\h
information accurately third feature exploration\h it's difficult to find or focus on a\h\h
single characteristic in multi-layered data\h sets we use distance to tactile encoding to\h\h
tackle this problem the distance to tactile encoding\h is a technique that provides distance information\h\h
through tactile channels stronger haptic sensation\h implies that the target is closer weaker sensation\h\h
suggests that the target is farther User Study 1\h Performance of Mid-Air Haptic Feedback this\h\h
study was designed to understand the effect of\h media haptic feedback on occlusion problems under\h\h
noisy conditions we recruited 16 participants\h for this task with a mean age of 25 years\h\h
we conducted this experiment in two modalities\h pure visual and visual + haptics and
under two noisy conditions high and low this was\h done for eight repetitions the task was to find\h\h
the highest density cluster among the five\h clusters as fast and accurately as possible\h\h
we recorded the completion time and answer\h of each participant for result analysis\h\h
results clearly show that visual plus haptic\h condition delivered higher accuracy in both\h\h
conditions and noise was a significant factor\h with respect to cluster identification accuracy\h\h
in terms of completion time participants took\h less time in visual + haptic scenario and\h\h
noise was not a significant factor with respect to\h completion time which was an interesting finding\h\h
we collected some subjective responses as well\h from the participants they were collected in the\h\h
two areas first understanding the densities\h of cluster second overcoming the problem of\h\h
occlusion visual + haptic condition was\h preferred in both density and occlusion and\h\h
modality was a significant factor User Study 2\h Effect of Spatial Position Offset the study was\h\h
designed to identify the strengths and weaknesses\h of direct and indirect interaction modes\h\h
we recruited 16 participants for this task with\h the mean age of 26 years the study consisted\h\h
of two tasks TaskLungs: Find the biggest cancer\h tissue in the human lung dataset TaskScar: Find the\h\h
scar in human head dataset consisting of three\h layers skin muscle and skeleton study was compared\h\h
among direct and indirect interaction mode with\h eight repetitions we recorded the completion\h\h
time and answer of each participant for result\h analysis in TaskLungs Direct Touch mode performed\h\h
better in both accuracy and completion time in\h TaskScar Indirect Touch mode performed better with\h\h
respect to completion time but the accuracy was\h comparable among both modes after examining the\h\h
results of both experiments we found that bare\h hand interaction with mid-air haptics helps\h\h
in understanding complex 3d data sets tangible\h interactions mitigates the problem of occlusion\h\h
and improves accuracy and efficiency direct touch\h mode performs better when analyzing multiple small\h\h
areas and indirect touch mode performs better\h when exploring a single significant characteristic\h\h
thank you for listening please\h feel free to ask any questions