- So this is Min. I will have a presentation on
assessing stroke rehabilitation exercise,
which is jointly done by Dan, Asim, Alex, and Sergi.
And this presentation I will briefly describe
how computers system can support assessing rehabilitations.
And I will describe the design of the stroke
rehabilitation exercise, rehabilitative study,
and I will propose, the third, about assessing between
particularly assessing the stroke rehabilitative exercise
and our validation research, conclusion, and future work.
So post-stroke survivor require repetitive
physical therapeutic session.
However, this is expensive and time consuming
because it require presence of the therapist
for assessment and generating useful feedback.
And as a therapy has a limited availability,
they often prescribe in-home rehabilitation
where patient has to perform
prescribe exercise themself.
And during in-home rehabilitation,
patient often become unmotivated or
confused whether they correctly following the
prescribed exercise.
Also therapy's have a problem with
tracking the patient's progress
over the time.
So, researchers attempt to improve
the rehabilitation practices with
a computer technologies.
So first, they try to log and analyze sensor data
to track the exercise metrics such as
join angle and number of repetition.
However, according to the user study
the therapy's considered that
analyzing sensor data is still not intuitive
to understand individuals' patient's performance.
And another approach is to classify
the occurrence of the incorrect motion
with sensor data and machine learning.
However, the problem is this won't provide
the intuitive, the detection, the problem is that
this simple binary detection cannot provide
the diverse level of the patient's performance.
So there is a need of quantifying exercise performance,
and researchers start exploring the usefulness
of kinematic variable for clinical assessment.
And for example like a correlate a kinematic variable
with clinical score, but still the limited
work hasn't explored how we could actually
quantify and it works compared to the human.
This research of proposed development of
intelligent therapist which can describe the exercise,
and observe the patient's exercise performance
with a kinetic sensor and summarize
exercise performance with the machine learning
and deliver some suggestion for improvement.
So this is a running example and I have demo,
but I will skip for the sake of time.
And in this paper I'll mainly address
following three questions:
The first one will be, how we can actually automatically
assess the exercise performance?
And second one is like, how well it could align
compared to the therapist evaluation?
And third, does each computed score has any
clinical relevance?
Now, I will describe about our design of stroke rehab study.
So we mainly utilize a three task-oriented
upper limb exercise.
Bring a cup to the mouth,
where the patient has to grab a cup,
and reaching to the mouth area as they're
drink the water.
And exercise two is the patient
has to reach forward to switch a light on.
Exercise three, they need to extend the elbow
to move the cane forward.
And for the evaluation of the stroke rehabilitation,
we have limitation of exercise.
We utilize three performance components
range of motion, smoothness, and compensations.
And range of motion indicates how well
individual achieved a task.
And smoothness describe the degree of
the trembling motions.
Compensation is to detect whether
there is unnecessary joint motion to compensate
patient's limited functional ability.
For example, when patient here is doing
a bring a cup to the mouth.
Compared to the unaffected side, affected side
of patient start raising the shoulder.
Due to his limited ability
on his reach position.
And given this performance components
we have also the therapist's scoring guideline
on a three point ordinal scale
which can be also be mapped into binary label
the correct or incorrect.
And those are basically, based on the clinical
evaluate stroke rehab assessment tool
of motor function task and Fugl-Meyer Assessment.
And now I will describe about our purpose of
automated assessment method.
So to summarize our overall flow.
So given an exercise trial, we will try
the joint position and extract feature,
and we'll train a binary classifier
to detect the occurrence of the incorrect motion.
And, then leveraging the binary classifier
we'll implement a threshold model to quantify
the performance score.
And now, I'll first describe about
the feature extraction part.
So, we track like 16 joints,
and we compute multiple
kinematic features like joint angle indicates angle
between three selected joints, a relative trajectory
indicates the Eucladian distance,
and projected trajectory indicates absolute distance
in particular axis, and
we also have some speed related measurement
and some normalization to compensate individual difference.
And given this feature, we also have
these three performance components
so we ended up extract a number
of feature for each of them.
The range of motion is composed of joint angle
and distant measurements, and smoothness
is mainly composed of the speed related one
on the elbow and the wrist joints,
and the compensation is composed of the
joint angle and distance to represent
the occurrence of the compensate motions.
And then, basically.
Given this feature set we compute the statistics
over the frames to summarize the exercise motion.
The rational is that here for example,
given the unaffected and affected side
we can see ultimately distinguish two sides
based on checking the maximum points.
So we compute maximum, minimum, and standard deviation,
average, and range given the feature
that I described earlier.
And now, I will describe about the classifier
and implementation of the threshold model.
So for binary classification, we implement the
traditional linear regression, decision tree,
SVM, neural network, and LSTM on
multiple limited parameter.
And we did just a quick search to pick
the best performing one.
And given the binary classifier,
here I describe about quantifications.
So basically, quantification is our intuition
is an intermediate problem between
classification and regression.
Where we need to predict a category
called performance scores like zero = motion,
one = limited, two = full normal motion.
So this is like a ordinal regression problem,
and one popular approach is a threshold model
with a binary classification.
Where the well-tuned binary classification
can provide the ranking score which
can be leveraged with a set of threshold values
to estimate a category called response.
So we implement this threshold model with
the binary classification for quantification.
Now I will describe about our validation study.
So we recruited 15 post-stroke survivors and
11 healthy subjects.
Totally we collected the 485 trial
of exercise for each one.
And then for generation of the ground truth,
we recruited the two therapists, primary and secondary.
The primary is referring to the one who
supported the recruitment of the patient,
and evaluate their functional ability.
And secondary therapist has no prior
interaction with the patient.
And then, they ended up performing two evaluation.
One individually watched their recorded video
and assigned score based on the scoring guideline.
And then, they had one hour discussion to share
their knowledge about the assessment to
reduce some of the disagreement, and did
another evaluation phase.
And basically, the rehabilitation process,
mainly managed by the single therapist
from beginning to the end.
So we decided to pick the primary therapist
as our ground truth score.
And this table summarize our research about
binary classification.
So we just use the leave-one-subject-out
cross validation, where we trained the
model without leaving the testing subjects.
Applied the testing on these, held out the subjects.
And here we compare the F1 Score on multiple models,
and overall we found that the neural network
and decision tree works well.
We expect the LSTM, but we found that it
is a bit difficult to derive the well-tuned model.
And given this outperforming the neural and decision
model, we implement the threshold model
and compare with the traditional multi-class
and regression one.
Overall, the threshold model seems to outperform
than the multi-class and regression.
And sometimes multi-class regression works
slightly better, but overall we can
observe that the threshold model is at least
the intermediate solution of multi-class
and regression.
When we applied the statistical significance testing
with a paired t-test, we found that threshold model
can generate equally good solution
with multi-class regression.
And the same trends can be observed
on the MSE value
And for answering the, how closely--
(white noise)