Welcome to the NeuMORE Project
Hemiparesis is the most common impairment after stroke, and the initial severity
of hemiparesis has been the strongest predictor of neural motor functional
recovery level. However, the intervention response of stroke survivors does not
always correlate with their initial level of impairment, which implies the
existence of some other factors that may significantly affect stroke survivors'
recovery process. It is critical to consider these factors in a principled,
comprehensive way, so that physical rehabilitation (PR) researchers may predict
which stroke survivors will respond best to therapy and, as a result, to
determine if a particular type of therapy is a more optimal match. Currently,
such prediction is mainly a manual process and remains a challenging task to PR
Based upon a domain-specific ontology, NeuMORE (NeuroMotor
Recovery Ontology), we propose a computing framework
that aims to facilitate knowledge acquisition from existing sources via
ontological techniques. It will assist PR researchers in better predicting
stroke survivors' neural motor functional recovery level, and will help physical
therapists customize most effective intervention therapy plans for individual
Challenges to be handled include, but not limited to:
- How to develop a formal knowledge
representation model in the domain of stroke recovery?
- How to combine the advantages from both top-down (knowledge-driven) and
bottom-up (data-driven) approaches during the ontology development?
- How will the ontological techniques help in mapping the electromyography (EMG)
and temporal-distance measures to motion of whole body center of mass (COM)?
- How to balance the pros and cons between a "shallow" semantic
annotation and a "deep" annotation?
- Is a centralized RDF data warehouse a better choice than a traditional relational data warehouse
for annotated sources?
- How to design a user-friendly interface that provides PR researchers a unified query-answering mechanism?
Our investigation in the aforementioned challenges is supposed to generate insights and broader impacts in the fields of
(1) biomedical informatics, (2) biomechanics, (3) stroke recovery, and (4) ontological techniques including semantic annotation & integration.