►
From YouTube: Fitness Fortuna
Description
Numenta HTM challenge
A
Hello,
everyone
I'm
because
and
I
have
teamed
up
with
Gopal
in
the
new
menta
hackathon
to
build
the
fitness
fortune
app.
This
is
an
iphone
app
and
an
Apple
watch
app
that
gave
if
I
sadness
it
uses
step
count.
Data
collected
from
Apple
watch
to
predict
future
steps
using
new
pics
HTM
predictive
algorithm.
A
When
we
looked
at
the
HTML
gerado,
we
were
impressed
with
the
predictive
data
that
it
is
able
to
generate
by
learning
simple
scalar
values
with
a
temporal
dimension.
While
we
were
brainstorming
for
ideas,
we
wanted
to
use
the
HTM
spread
of
capabilities
to
build
an
app
which
could
directly
influence
human
behavior.
A
We
thought
fitness
apps
would
be
a
great
use
case.
We
find
that
most
fitness
apps
today
are
reactive
and
not
predictive,
while
most
of
them
give
a
good
picture
of
what
fitness
activities
we
have
already
accomplished.
They
don't
quite
predict
into
the
future
to
warn
us
if
we
will
make
the
goal
or
not.
So
we
thought
why
not
do
that.
Fitness
for
china
aims
to
do
just
that
with
the
help
of
HTM,
so
I'm
going
to
now
demonstrate
the
fitness
fortunate
I
phone
app.
A
So,
as
you
see
here,
it's
requesting
me
access
for
health,
get
my
step
count
and
give
it
access
to
health
kit,
so
I'm
gonna
allow
it
and
I
reached
the
home
screen.
So
if
we
gonna
see
that
the
app
will
ask
me
to
do
three
things
first,
it
will
ask
me
to
enter
the
step
count
goal
per
hour.
As
you
see
here.
Second,
the
app
will
then
read
my
current
steps
accumulated
over
the
last
hour
and
then
use
HTM
in
the
backend
server
to
predict
my
step
count
in
the
next
hour.
A
Finally,
it
will
tell
me
natural
language,
if
I'm
going
to
make
my
goal
or
not,
and
then
it
will
encourage
me
to
challenge
it.
So,
let's
say
I
give
this
a
try,
I'm,
not
feeling
very
ambitious.
I
set
my
step
goals
to
just
40
per
hour
and
I'll
say
return
and,
let's
see
what
happens
so
as
I
entered
my
Harley
goal,
the
back
end
predicts
that
I
will
make
about
32
steps.
A
So
since
it's
still
less
than
the
hourly
co
that
I
just
said
it
wants
me
that
I
may
not
keep
up
with
my
step
goals.
So
finally,
it
urges
me
to
take
action
and
beat
its
prediction.
So
let's
say
I
take
a
walk,
oh
here,
and
there
and
I
feel
that
I
am
ready
for
a
challenge.
So
I'm
gonna
go
and
press
the
challenge
button,
and
we
tell
me
how
I
did
so
seems
like
in
the
last
one
hour.
I
ended
up
walking
126
steps,
so
it
is
happy.
A
I'm,
happy
and
I
did
end
up
beating
its
prediction
this
time.
So
this
is
how
the
fitness
fortunate
app
works.
I
can
always
come
back
for
another
challenge.
At
a
later
time.
Let's
say:
ma'am
I
am
trying
to
be
unreasonable
here
and
I.
Think
I'm
going
to
do
4,000
steps
in
an
arm.
So
let's
see
what
does
it
think
so
this
time
again
thinks
that
in
the
next
hour
the
I
might
end
up
just
doing
32
steps
and
it
still
I
just
need
to
take
the
challenge.
A
So
let's
say
for
the
next:
are
so
I
walk
a
lot
and
I
feel
I'm
up
for
the
challenge
and
I'm
going
to
go?
Give
this
a
try.
So
this
time
it
says,
unfortunately,
its
prediction
came
true.
Why?
Because
I
did
a
good
job,
I
did
walk
126
steps,
but
nevertheless
it
was
very
less
than
what
my
original
goal
was.
So
it
won
this
time.
So
I'd
now
like
to
demonstrate
the
Apple
watch
application.
B
B
It
still
thinks
that
my
credit,
it's
gonna,
predict
its
predicting
32
steps,
which
is
less
than
my
alley
goes,
so
it
thinks
I
may
not
be
able
to
make
it
so,
let's
say
I
take
a
walk
and
I
come
back
and
I
think
I'm
up
for
the
challenge,
so
I
tap
on
challenge
and
it
says
thumbs
up.
I
did
end
up
meeting
with
my
goals,
in
fact
exceeding
the
goals,
because
it's
more
than
my
article
that
I
set
up
so
I
can
always
come
back
to
another
round.
B
And
let's
say
this
time:
I
set
up
a
very
high
goal
and
instead
of
50
s,
we
find
it,
as
my
Ally
goes
so
you're
saying,
500
reflected
here
and
I'm
gonna
take
a
walk
for
some
more
time
and
Savin
myself,
and
this
time
it's
still
75.
That
I
ended
up
walking
the
last
hour,
but
since
its
lesser
than
the
hourly
goes,
it
shows
that
I
did
not
end
up
meeting
with
mango.
So
this
is
the
Apple
watch
app
demo.
A
C
Hi,
this
is
a
demo
of
the
server
side
in
cementation
of
fitness.
Fortuner
called
the
step
oracle.
The
step
oracle
runs
the
new
peak
HTM
model
with
a
scalar
encoder.
We
have
collected
approximately
one
week
worth
of
step
con
data
from
the
Apple
watch,
which
is
included
in
the
step
count,
data
or
CSV
file.
We
run
this
data
initially
through
you
pick
to
produce
the
plot
that
you
are
seeing
on
the
screen.
It
also
produces
an
output
file
which
includes
the
timestamp
predicate
value
and
a
normally
score
for
each
given
actual
value.
C
In
this
server.
We
host
a
UDP
server
to
listen
for
data
from
the
iphone,
which
is
the
client.
The
data
that
comes
in
from
the
iphone,
which
is
the
actual
step
count.
Value
is
fed
through
new
pic
to
generate
a
predicted
value
for
the
next
time
step.
The
predicted
value
is
sent
back
to
the
iphone
the
iphone,
which
runs
the
fitness
for
china
app
along
with
the
watch
app.
It
shows
this
uses
this
value
for
for
the
game
that
we
have
built
to
play
with
the
HD
mro.
C
As
you
can
see
here,
the
server
is
waiting
for
a
message
from
the
point.
Currently,
the
actual
version
of
the
server
is
running
in
an
amazon
ec2
instance,
but
for
this
demo,
I'm
running
the
server
on
a
local
host,
so
I
have
built
a
test
client
which
will
generate
a
set
of
messages
to
be
sent
to
the
server.
The
server
will
feed
it
through
the
new
pic
model
and
return
the
prediction
back
to
the
client,
so
that's
it
for
the
demo
of
fitness
for
China
I
hope
you
enjoyed
it.
Thank
you.