hello everyone my name is razer chen and
i will be presenting daedalus inner dark
designing for non-visual accessible
construction of laser card architecture
i would like to thank tom yeah and bing
yusun for guiding this project
and my colleagues sudan fungi and
seraphina for their hard work
let's look at how data loss works
data loss is a fusion 360 plugin in
dataless designers can add ground
support for the model in order to
stabilize the assembly process for blind
or low vision people
datas automatically generates an 2d
layout and comparing different tactile
ads and spatial arrangements such as
symmetry or layering order
designers can fine-tune the layout in
daedalus such as fitting on components
within the board
let us set constraints for the manual
edits in order to maintain the
accessibility heuristics they we
identify from a formative study
finally data loss generates a file ready
to print by a laser cutter
let's look at our first user study and
findings that inform the design of data
loss and the tactile ads
to understand the accessibility barriers
we first conducted a formative study
with seven blind participants
we started with the learning session
where our participants were introduced
to three commonly used joints including
finger joint slot joint and
multisentinent joint
by practicing some example models of
them
after familiarizing with the joints
participants were asked to assemble five
lexical models in a testing session
each of the five assembly tasks had
specified time thresholds
and will regulate the completion
accuracy of each task as performance
measurement
completion accuracy was measured as the
number of correctly assembled joints out
of the total joints in the model
finally we held a codicization where
participants were encouraged to speak
about the barriers they encountering in
testing session and potential solutions
to address them
we also provided some crafting materials
for our participants to express their
thoughts on designing tactile ads
our study identified a number of
difficulties for example participants
spend much time deciding which piece to
start with and most of them express
wanting to start from a core component
that can quickly form a stable structure
participants also have difficulty in
identifying the correct joint part two
pair
during assembly it's hard to stabilize
the mean assemble model when assembling
other pieces without vision
and the model was prone to collapsing
some models had symmetrical pieces which
took much time for our participants to
identify
unlike envision they need to touch every
detail to confirm the symmetry
some models require fixed assembly order
which was hard for blind participants to
observe
the wrong assembling would incur
additional effort in removing the
assembled pieces and reassembling
to mitigate these barriers we could
design the tactile edge with our
participants
we designed a large hollow circle on the
bottom right corner of the plate
which links to the first component with
a takedown light line
users can follow the tactile lines and
arrows to find component pairs
the area surrounding a joint is hollowed
out in health circle to allow components
to be easily lifted from the plant
after assembling the first few
components participants can press them
into the ground support to make the
remaining assembly more stable
to address the problem of fixed order
components are arranged from bottom up
and layered based on their proximity to
the entry component
users can also place the model back into
the plant to quickly access the tactile
hints
we also arrange symmetrical components
to be mirrored this way participants
don't need to spend additional time
finding symmetrical pieces
here we briefly describe the rationale
of layout generation
given input model and joined inputs
each component is taken as a vertex
including the ground support and each
joint is taken as age to build an
undirected graph
based on this graph we conduct breadth
research to build the tree
finally we add a ground support to the
tree which will be layered above the two
components it connects with
given this assembly graph
we use a star search to find the optimal
path for engraving tactile lines
we can finally generate a layout in
fusion 360 for filler edits by the
designer
detection of joint types their length
and location is required for more
accurate integration of textile ads
our method is to classify joint pattern
of the endpoints of the contours
more details can be found in our paper
to evaluate that tactile ads we ran
another study with eight blind
participants
we had a learning session to introduce
the three most commonly used joints same
as in the first study
and also introduced all tactile ads we
designed
in the testing session participants were
also asked to assemble the same five
models from the first study
but this time they used the plaid and
tactile ads generated by doodles
in this evaluation we found that
participants were able to follow the
tactile lines and arrows to find the
joint pairs
with the ground support the mid-symbol
model remained stable during assembly
we also found that participants were
able to place the model back to excess
pairing hints more efficiently
these are the results of our two studies
the bars represent the completion
accuracy of each task
by using our designed tactile ads
participants in second study had better
performers overall on the assembly tasks
feel free to read our paper for more
details thank you for listening