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Research Project
The AHA project aims to develop a robotic assistance plataform for supporting healthy lifestyle and sustain active aging.
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Authors
Publications
Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment
Publication . Lee, Min Hun; Siewiorek, Daniel P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, Sergi
Clinical decision support systems have the potential to improve work flows of experts in practice (e.g. therapist’s
evidence-based rehabilitation assessment). However, the adoption of these systems is challenging, and the
gains of these systems have not fully demonstrated yet. In this paper, we identified the needs of therapists to
assess patient’s functional abilities (e.g. alternative perspectives with quantitative information on patient’s
exercise motions). As a result, we co-designed and developed an intelligent decision support system that
automatically identifies salient features of assessment using reinforcement learning to assess the quality
of motion and generate patient-specific analysis. We evaluated this system with seven therapists using the
dataset from 15 patients performing three exercises. The results show that therapists have higher usage intent
on our system than a traditional system without patient-specific analysis (𝑝 < 0.05). While presenting richer
information (𝑝 < 0.10), our system significantly reduces therapists’ effort on assessment (𝑝 < 0.10) and
improves their agreement on assessment from 0.66 to 0.71 F1-scores (𝑝 < 0.01). This work discusses the
importance of human centered design and development of a machine learning-based decision support system
that presents contextually relevant information and salient explanations on its prediction for better adoption
in practice.
Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment
Publication . Lee, Min Hun; Siewiorek, Daniel P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, Sergi
Automated assessment of rehabilitation exercises using machine
learning has a potential to improve current rehabilitation practices.
However, it is challenging to completely replicate therapist’s deci sion making on the assessment of patients with various physical
conditions. This paper describes an interactive machine learning
approach that iteratively integrates a data-driven model with ex pert’s knowledge to assess the quality of rehabilitation exercises.
Among a large set of kinematic features of the exercise motions, our
approach identifies the most salient features for assessment using
reinforcement learning and generates a user-specific analysis to
elicit feature relevance from a therapist for personalized rehabilita tion assessment. While accommodating therapist’s feedback on fea ture relevance, our approach can tune a generic assessment model
into a personalized model. Specifically, our approach improves
performance to predict assessment from 0.8279 to 0.9116 average
F1-scores of three upper-limb rehabilitation exercises (𝑝 < 0.01).
Our work demonstrates that machine learning models with feature
selection can generate kinematic feature-based analysis as expla nations on predictions of a model to elicit expert’s knowledge of
assessment, and how machine learning models can augment with
expert’s knowledge for personalized rehabilitation assessment.
An exploratory study on techniques for quantitative assessment of stroke rehabilitation exercises
Publication . Lee, Min Hun; Siewiorek, Daniel P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, Sergi
Technology-assisted systems to monitor and assess rehabilitation
exercises have an opportunity of enhancing rehabilitation practices
by automatically collecting patient’s quantitative performance data.
However, even if a complex algorithm (e.g. Neural Network) is
applied, it is still challenging to develop such a system due to pa tients with various physical conditions. The system with a complex
algorithm is limited to be a black-box system that cannot provide
explanations on its predictions. To address these challenges, this
paper presents a hybrid model that integrates a machine learn ing (ML) model with a rule-based (RB) model as an explainable
artificial intelligence (AI) technique for quantitative assessment of
stroke rehabilitation exercises. For evaluation, we collected thera pist’s knowledge on assessment as 15 rules from interviews with
therapists and the dataset of three upper-limb stroke rehabilitation
exercises from 15 post-stroke and 11 healthy subjects using a Kinect
sensor. Experimental results show that a hybrid model can achieve
comparable performance with a ML model using Neural Network,
but also provide explanations on a model prediction with a RB
model. The results indicate the potential of a hybrid model as an
explainable AI technique to support the interpretation of a model
and fine-tune a model with user-specific rules for personalization.
Towards personalized interaction and corrective feedback of a socially assistive robot for post-stroke rehabilitation therapy
Publication . Lee, Min Hun; Siewiorek, Daniel P.; Smailagic, Asim; Bernardino, Alexandre; Bermúdez i Badia, Sergi
A robotic exercise coaching system requires the
capability of automatically assessing a patient’s exercise to in teract with a patient and generate corrective feedback. However,
even if patients have various physical conditions, most prior
work on robotic exercise coaching systems has utilized generic,
pre-defined feedback.
This paper presents an interactive approach that combines
machine learning and rule-based models to automatically assess
a patient’s rehabilitation exercise and tunes with patient’s
data to generate personalized corrective feedback. To generate
feedback when an erroneous motion occurs, our approach
applies an ensemble voting method that leverages predictions
from multiple frames for frame-level assessment. According to
the evaluation with the dataset of three stroke rehabilitation
exercises from 15 post-stroke subjects, our interactive approach
with an ensemble voting method supports more accurate frame level assessment (p < 0.01), but also can be tuned with held-out
user’s unaffected motions to significantly improve the perfor mance of assessment from 0.7447 to 0.8235 average F1-scores
over all exercises (p < 0.01). This paper discusses the value of
an interactive approach with an ensemble voting method for
personalized interaction of a robotic exercise coaching system.
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Funders
Funding agency
Fundação para a Ciência e a Tecnologia
Funding programme
Funding Award Number
SFRH/BD/113694/2015