1 research outputs found
An exploratory study on techniques for quantitative assessment of stroke rehabilitation exercises
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.info:eu-repo/semantics/publishedVersio