Advanced Techniques for Robotic Assessment of Neurological Impairments In Stroke Patients
Authors
Tyryshkin, Kathrin
Date
2014-09-29
Type
thesis
Language
eng
Keyword
task analysis , data mining , fuzzy neural network , feature selection , stroke assessment , task comparison , feature information , robotic technology
Alternative Title
Abstract
Stroke is an acute injury of the central nervous system and is caused by the disruption of blood flow or by the rupture of blood vessels. A stroke can impact many body functions, often causing motor, speech, memory, vision and other sensory impairments.
It is highly prevalent and often requires long hospitalizations and post-stroke rehabilitation.
The key to successful rehabilitation after stroke is an accurate assessment
of the impairment. Current clinical assessments of stroke-related impairments involve physical assessment and visual observation by physicians. Existing clinical scores of upper limb function often use observer-based ordinal scales that are subjective and
commonly have floor and ceiling effects. Therefore, these methods are not adequate
to reliably discriminate different levels of performance.
Robotics and integrated virtual reality systems have a tremendous potential to be
used in computational systems that analyze, visualize and aid clinicians to identify
and assess sensory-motor impairments. This thesis presents a framework for analysis
and extraction of reliable and valid features from robotic data that can be used
to accurately and objectively assess neurological impairments. The framework was
applied on the Object Hit task that assesses the ability of participants to select and
engage motor actions with both hands.
In addition, the Object Hit Task was compared to the Object Hit and Avoid task, which is a slight modification of the original task. The comparison was done
using a developed feature and task analysis framework. This framework encompasses
similarities and differences between tasks in a given experiment in terms of feature
information. The results showed that for the data used in the analysis, Object Hit
task is able to identify impairments more effectively than the Object Hit and Avoid
task. The overall results demonstrate that the Object Hit task provides an objective
and easy approach to quantify upper limb motor function and visuospatial skills after
stroke. The developed assessment tool can also be applied for diagnosis and prognosis
of other neurological deficits, beyond stroke assessment.
Description
Thesis (Ph.D, Computing) -- Queen's University, 2014-09-29 17:06:09.217
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