Abstract:
In
recent years, the increase in the elderly population has placed
significant burdens on post-rehabilitation schemes, resulting in high
logistical costs and considerable social impacts due to hospitalization
or frequent visits. These challenges call for a transformation in the
traditional approach to physical patient care, which can be achieved by
leveraging the Internet of Medical Things (IoMT), particularly through
the use of pervasive wearable sensors. When attached to patients during
treatment or therapy, these sensors can provide valuable supplementary
information to healthcare professionals. When it comes to adopting IoMT
technologies, cost efficiency, portability, and generalization are key
factors. Specifically, this study aims to enhance the cost-effectiveness
and versatility of wearable eHealth monitoring architectures that
utilize foot pressure sensing hardware for the motor assessment of
post-stroke and neurologically impaired patients. It leverages lower
limb IMU sensory information and machine learning to mitigate the
reliance on foot pressure sensing hardware. We demonstrate the potential
of Artificial Intelligence (AI) in predicting fine-scale foot pressure
using only inexpensive, off-the-shelf motion sensors. We propose a
self-supervised, exercise-agnostic asynchronous foot pressure decoding
model that does not require human annotation. The algorithm is
thoroughly evaluated using appropriate performance metrics, and our
experimental tests show promising results.
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