T
his
paper
tackles the problem of automatic detection of knee osteoarthritis.
A computer system is
built that takes as input the body kinetics and produces as output
not only
an estimation of presence
of the knee osteoarthritis
,
as previous
ly
do
ne in
the literature
, but also
the most discriminating
parameters along with
a set of rules on how this decision was reached.
This
fills the gap of
interpretability between the medical and the
engineering
approaches
.
W
e collected locomotion data
from 47 su
bjects with knee osteoart
hritis and 47 healthy subjects.
Osteoarthritis subjects were
recruited from hospital clinics and GP surgeries, and age and sex matched heathy subjects from the
local community
.
S
ubjects walked on a walkway
equipped
with two force p
lates with
piezoelectric 3
-
component force sensors
.
Parameters of the vertical, anterior
-
pos
terior, and medio
-
lateral ground
reaction forces,
such as
mean value, push
-
off time, and slope
,
were extracted.
Then r
andom forest
regressors
map those
parameters
v
ia rule induction
to
the degree of kne
e osteoarthritis.
To boost
generalisation ability
,
a subject
-
independent protocol is employed.
The 5
-
fold cross
-
validated
accuracy is 72.61%±
4.2
4%.
W
e show that with 3 steps or less
a reliable clinical measure can be
extracted
in a rule
-
based approach
when the dataset is analysed appropriately.