Unknown: Hello, my name is Winston. I'm here to present my
work SenseCollect and to share my experience on how to collect
on-body sensor-based human activity data. So, human
activity recognition is we call the HAR is a very important
topic in Ubicomp community It has a wide variety of
applications. However, in contrast to breakthroughs
evident in other fields, with large amounts of labeled data,
accessable on-body sensor-based HAR data is very limited. For
example, computer vision, they have ImageNet data set, it has
14 million images. For NLP, they have dataset called 1 billion
words benchmark. It literary has 1 billion words. But for the
sensor-based HAR data, the two largest accessable we have now,
one is only five hours of data. Another one is only 7.5 hours,
and neither of them has more than 10 participants. So this
very limited dataset really affect the performance of HAR
technology. Many researchers tried to solve this problem by
generating simulated IMU data from videos. Or they do data
augmentation, GANs, transfer learning. In this paper, instead
of simulating the problem around model development, we take a
more grounded approach, we try to understand what is the
difficulties of this HAR data collection and what makes a good
HAR dataset and how can we collect large amounts of data
set efficiently through a set of structured guidelines. In the
study process procedure, we have three steps. First, we have a
preliminary study, try to understand the current practice
of data collection and second step by designing a data set and
in the third step, and we try to collect this dataset and deliver
our findings and theory about data collection. So, in the
preliminary study, we survey and interview many participants,
many researchers from different applications in HAR area, and we
find out it is very difficult to collect this kind of data, I
still remember one PhD student, he told me he is fourth year
student and but he spent two years doing nothing just
collecting data. And most of the time the data he collect is
useless useless, because the quality is very poor. So he has
to recollect data again and again. So data collection is
very boring, time-consuming, error-prone, and a lot of
difficulties. So and then I designed a data set. And the
details of why I designed this dataset ,please go to my paper.
And basically this is a finger movement data set to try to
recognize your finger writing with a smartwatch. And I make an
app on the smartwatch to collect this IMU data for this finger
movement vibration, and I make an app on the laptop. So it
helps give the instruction to the participants to know what
kind of letters or numbers they need to write, and also provide
a visual feedback about the real time data output. The deployment
study, usually we know the researchers, they try to collect
data in the way they will collect data directly by
themselves. So they recruited participants, and then they
monitor, supervise, advice these participants to collect data
very carefully to secure the data quality. And also they ask
their lab mates, or ask their friends to collect data. And
also they will monitor them to secure the data quality.
However, this most common ways turn out the result is very
terrible based on our experiment, and we only collect
a few data and most of the data quality is very poor. So we
think about, can we just ask many investigators, we recruit
many investigators, we give them the devices and ask the
investigator to recruit other participants to collect data at
the same time. So in this way, we can collect so many data from
the investigators. However, you may say this is impossible, it
is so difficult for us, researchers ourselves to collect
data already, how can we ask the investigators to collect good
quality data. So as you can see here, so the data we want to
collect is on the left side, but actually the data we collect, we
have usually in the right side it has many errors in the data
in the data, so we cannot label it correctly.
And then I designed a control variable experiments to
understand why it is impossible and it is why it is difficult to
recruit the investigators to collect data or why it is
difficult for participants to collect data. Due to the time
limitation I will just talk about the findings and theories
here. So it turns out monitoring is not necessary. To secure the
data quality, we only need to ask the participant, we only
tell the participant that if the data quality is too poor, we
don't pay to. So in this way, the participants, they will try
their best to give you the good quality data. This is very
similar to the factory. So when the when we recruit many people
to monitor the workers to make them to make more products and
make good quality products, but it usually doesn't work well. So
they tried to pay their workers by counting the numbers of
products they have they made or the quality of the products they
made. So similarly, we pay the participants by the amounts of
the data they collect, and the quality of the data they
collect. And there are also some other findings we find out, we
can help the participants to collect more good quality data
and more efficiently. One is the sensing feedback is very
important. So the visual feedback, we design on the
laptop or the smart glasses, we provide the sensor data
feedback, so without this, the participants they don't know
they're doing well, or they're not doing well. And this really
affects the data quality. And another challenge is it is very
boring for the participants to collect data, this tedious data
collection, hours and hours. So we play some music, and it is
helpful to make them feel better. And also we thinking
maybe we can design games to help them to to do this human
activity. And that this is not boring, so they can collect data
hours and hours and focus on this boring task. And another
thing is be careful if they collect data for hours and hours
without any break without any rest, this will also affect the
data quality. So through these findings, we make this
instruction. And we collect this finger writing data of 192 hours
from 32 participants just in one week. So this is much much
larger than the accessable HAR data we have nowadays. And w
also evaluate the accur cy the quality of this data
et offline, and as well as in re l world. And to test the
generalizability, based on th se guidelines, we collect an
ther HAR data set. And in one we k, we collected 113 partici
ants data. So we do wish in the future, based on these g
idelines, it can help the res archers to collect data easily,
to collect more data, and to c llect more good data. And thi
can advance the HAR te hnology. Thank you