hello everyone my name is Sayma Akther. I am going to present mTeeth for detecting brushing
teeth surface from wrist-worn initial sensor I would like to thank all of my co-authors
for their contributions my professors nih nsf and oralb for supporting my work
Recent dental studies show that in a brushing episode surprisingly more than 70 percent area
of the teeth people do not clean properly that's why most of the people wonder why they have teeth
decay even though they brush their teeth regularly naturally some question arises that need to be
answered such as do or do all of our teeth get enough brushing do we miss some teeth or overbrush
some teeth frequently so therefore we need a system to characterize the our brushing session
several attempts are done with instrumented and smart toothbrush but they rely on user input and
more than 80 percent people in the united states are still use manual toothbrushes in this work we
focus on characterizing manual toothbrushing using wrist worn inertial sensor at first we define the
surface region since people brush group of teeth simultaneously so we divide the whole teeth
area into three broder categories and name each teeth surface by combination of three categories
as a result we have nine teeth surfaces so the problem is can we detect brushing teeth surfaces
from wrist worn inertial sensors in our model we use three axis accelerometer and gyroscope
which is included most of the activity tracker and smartwatches the figure shown one brushing
episode with annotation to solve this problem we faced several challenges we need fine grained
level data as shown in the figure and need tight time synchronization to avoid ambiguous labeling
time series segmentation is also major challenges since duration of surface varies a lot
finally we need to extract informative features from this noisy signal and between
percent variability is very high to get the granular level data we enriched moral data set
where videos are captured during brushing two independent coders are carefully annotated each
surface transition from each video in total more than ten thousand labels are annotated
now i present the pipeline of our model due to the time constrain i will explain the main three
component of this timeline in our level data we observe time spent on a bashing surface varies
from a few millisecond to several seconds this poses a significant challenge to find an optimal
window if we choose our window size smaller we might have insufficient amount of data to find
the distinctive features if we choose our window size larger it may create ambiguity and mismatch
in labeling therefore we seek dynamic dynamic length event based approach for segmentation
during brushing people generally perform either up and down or back and forth periodic motion
with the brush which reflect in the accelerometer signal as peek and valley which as shown in the
figure we identify one valley peak valley corresponds to one brushing stroke we identify
one brushing stroke as the anchor event which is efficiently detectable and prevent ambiguity
from each brushing stroke we computed several feature including accelerated gyroscope and
orientation feature after that we apply correlation based feature extraction to
extract most important feature after extracting most important feature we want to classify each
brushing stroke to any of the nine surfaces since inner surface brushing is completely
different than outer surface brushing we created layer by layer bayesian network
where the first layer tries to identify the likelihood of inner versus outpur the second layer
tries to identify the likelihood of center left light given the condition of first layer and so on
finally model classifies the class with maximum likelihood furthermore we also observe the stable
pattern during the surface transition that's why we updated the network by adding state transition
we perform the series of evaluation first we want to evaluate the accuracy of detecting brushing
stroke we see the test difference between actual count and estimated count is varied
within 2 and min absolute error is 4.2 percent which is much better than acoustic based method
then even we want to evaluate the between percent generalizability for that we started with leave
one subject out cross validation get a f1 score of 65 percent for personalized model we perform
leave one episode out cross validation to see the impact of within person variability get a
f1 score of 80 percent we also performed 10 fold cross validation to compare with the recent work
get f1 score of 86 percent up to now i have presented the accuracy of 9 surface detection
but for oral health purposes both user and dentist might be interested to know the total
brushing duration on each surfaces recall the initial questions what we wanted to know from
each brushing episode even though we applied leave one subject out cross validation mean absolute
error is less than 7.5 percent so the agreement is more than 92 percent in summary it is the first
to work for detecting brushing stroke from wrist worn inertial sensor which create an opportunity
to detect the correct way of brushing in future also we present stroke wise surface detection
but our video annotation did not differentiate between inner and chewing surfaces also present
time synchronization in the paper which can be extended to other behaviors interested
researchers are welcome to check the method in the paper thank you