This video will present a hybrid approach that integrates machine learning model with
a rule-based model from therapists as an explainable AI technique for quantitative assessment of
stroke rehabilitation exercises.
Rehabilitation assessment is critical to determine interventions for patients with neurological
and musculoskeletal problems (e.g. stroke).
However, it is infrequently performed due to the limited availability of therapists.
To address this problem, researchers have explored the feasibility of automatically
monitoring and assessing patient's exercises using computational models.
These computational models can be categorized into either a rule-based or a machine learning
approach.
A rule-based approach involves domain experts to review sensor data and derive a set of
monitoring rules.
This rule-based approach is interpretable and flexible to develop a customized model.
However, it is difficult to articulate expert's decision making into a set of rules and time-consuming
to review a large amount of sensor measurements.
Alternative approach is to utilize a machine learning algorithm with labeled sensor data.
This machine learning approach has the benefit of automatically learning a function with
good performance.
However, this approach with a complex algorithm cannot provide explanations on its predictions
to support therapist's decision making, which can impede the adoption of these technologies
in practice.
In this paper, we present a hybrid approach that combines the benefit of machine learning
and rule-based models to assess the quality of rehabilitation exercises.
To demonstrate our approach, we specify a test domain as stroke, which is the second
leading cause of death and the third most contributor to disability.
Based on iterative discussion with three therapists, we specified three upper-limb exercises
and three performance components for stroke rehabilitation assessment.
For implementation, we represent an exercise motion with sequential joint positions from
a Kinect 2 sensor and extract various kinematic features.
For instance, these features include joint angles, the euclidean distance between two
joints and speed related features.
For modeling patient's exercises, this approach applies supervised learning algorithms with
labeled sensor data.
For algorithms, we explore decision tree, linear regression, support vector machine,
and neural network.
For the development of a rule-based model, we interviewed to derive fifteen independent
rules for assessment.
For example, given bring a cup to the mouth exercise, where
a patient has to raise patient's write to the mouth,
therapists simply check whether the position of the wrist exceeds the top of the spine
to assess the range of motion.
Thus, such feature-based rules are generated during the interview.
For a hybrid model, we apply a weighted average to combine machine learning and rule-based
models from therapists.
For the evaluation, we collected the dataset of three upper-limb exercises from 15 post-stroke
and 11 healthy subjects.
Two therapists annotated this dataset to implement our approach and compute therapist's agreement
level.
We evaluated models with leave-one-subject-out cross validation and compared them in term
of performance to replicate therapist's assessment and their interpretability.
First, this orange dotted line indicates the therapist's agreement level.
For machine learning models, we found that the performance of a model is
inversely proportional to the interpretability of a model.
Machine learning models with complex algorithms (e.g. support vector machine and neural network)
perform better than machine learning models with interpretable algorithms (e.g. decision
tree and linear regression) while sacrificing the interpretability of a model.
Overall, neural network outperformed other algorithms and therapist's agreement level.
In contrast, a rule-based model has the lowest performance as it involves a set of rules
that are not tuned for individuals.
But, it has the highest interpretability by directly involving the end user.
For a hybrid model, we combined a machine learning model with neural network with a
rule-based model.
As a rule-based model has the lowest performance, combining these two models does not lead to
performance improvement.
However, a hybrid model still performs better than other algorithms except neural network.
Even if a hybrid model has slightly lower performance than neural network, it provides
the potential benefit of analyzing rules and fine-tuning a model with patient-specific
rules.
Thus, it has higher interpretability than a neural network or support vector machine.
To conclude, we present a hybrid model that combines machine learning and rule-based models
and demonstrate its potential in terms of high performance, which is comparable to neural
network and therapist's agreement level and partial interpretability.
In future, we will explore the feasibility of tuning this partially interpretable hybrid
model for personalized assessment.
Thank you for your attention.