Stroke is a major cause of death and disability worldwide. The damage or death of
brain cells caused by a stroke affects brain function and leads to deficits in sensory
and/or motor function. As a consequence, a stroke can have a significantly negative
impact on the patient’s ability to perform activities of daily living and therefore also
affect the patient’s quality of life. Stroke patients may regain function through
intensive physical rehabilitation, but often they do not recover their original
functional level. The incomplete recovery in some patients might be related to e.g.
stroke severity, lack of motivation for training, or insufficient and/or non-optimal
training in the initial weeks following the stroke.
A threefold increase in the number of people living past the age of 80 in 2050,
combined with the increasing number of surviving stroke patients, will very likely
lead to a significant increase in the number of stroke patients in need of
rehabilitation. This will put further pressure on healthcare systems that are already
short on resources. As a result of this, the amount of therapeutic supervision and
support per stroke patient will most likely decrease, thereby affecting negatively the
quality of rehabilitation.
Technology-based rehabilitation systems could very likely offer a way of
maintaining the current quality of rehabilitation services by supporting therapists.
Repetition of routine exercises may be performed automatically by these systems
with only limited or even no need for human supervision. The requirements to such
systems are highly dependent on the training environment and the physical and
mental abilities of the stroke patient. Therefore, the ideal rehabilitation system
should be highly versatile, but also low-cost. These systems may even be used to
support patients at remote sites, e.g. in the patient’s own home, thus serving as telerehabilitation systems.
In this Ph.D. project the low-cost and commercially available Microsoft Kinect
sensor was used as a key component in three studies performed to investigate the
feasibility of supporting and assessing upper limb function and training in stroke
patients by use of a Microsoft Kinect sensor based tele-rehabilitation system. The
outcome of the three studies showed that the Microsoft Kinect sensor can
successfully be used for closed-loop control of functional electrical stimulation for
supporting hand function training in stroke patients (Study I), delivering visual
feedback to stroke patients during upper limb training (Study II), and automatization
of a validated motor function test (Study III).
The systems described in the three studies could be developed further in many
possible ways, e.g. new studies could investigate adaptive regulation of the intensity
used by the closed-loop FES system described in Study I, different types of feedback
to target a larger group of stroke patients (Study II), and implementation of more
sensors to allow a more detailed kinematic analysis of the stroke patients (Study III).
New studies could also test a combined version of the systems described in this
thesis and test the system in the patients’ own homes as part of a clinical trial
investigating the effect of long-term training on motor function and/or non-physical
parameters, e.g. motivational level and quality of life.[…]
via Link to publication from Aalborg University
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