If we insist upon not preventing the neuronal cascade of death, we will need to get much much better at rehabbing and the only way to do that is to be able to objectively specify the muscle problems that are occurring during daily life. This would seem to be an excellent way to do that, then our therapists could take these readings and come up with protocols to address every problem. Damn this is so simple, why does it take a stroke-addled person to think of this easy solution?
Monitoring motor capacity changes of children during rehabilitation using body-worn sensors
Abstract (provisional)
Background
Rehabilitation services use outcome measures to track motor performance of their patients
over time. State-of-the-art approaches use mainly patients' feedback and experts'
observations for this purpose. We aim at continuously monitoring children in daily
life and assessing normal activities to close the gap between movements done as instructed
by caregivers and natural movements during daily life. To investigate the applicability
of body-worn sensors for motor assessment in children, we investigated changes in
movement capacity during defined motor tasks longitudinally.
Methods
We performed a longitudinal study over four weeks with 4 children (2 girls; 2 diagnosed
with Cerebral Palsy and 2 with stroke, on average 10.5 years old) undergoing rehabilitation.
Every week, the children performed 10 predefined motor tasks. Capacity in terms of
quality and quantity was assessed by experts and movement was monitored using 10 ETH
Orientation Sensors (ETHOS), a small and unobtrusive inertial measurement unit. Features
such as smoothness of movement were calculated from the sensor data and a regression
was used to estimate the capacity from the features and their relation to clinical
data. Therefore, the target and features were normalized to range from 0 to 1.
Results
We achieved a mean RMS-error of 0.15 and a mean correlation value of 0.86(p<0.05 for
all tasks) between our regression estimate of motor task capacity and experts' ratings
across all tasks. We identified the most important features and were able to reduce
the sensor setup from 10 to 3 sensors. We investigated features that provided a good
estimate of the motor capacity independently of the task performed, e.g. smoothness
of the movement.
Conclusions
We found that children's task capacity can be assessed from wearable sensors and that
some of the calculated features provide a good estimate of movement capacity over
different tasks. This indicates the potential of using the sensors in daily life,
when little or no information on the task performed is available. For the assessment,
the use of three sensors on both wrists and the hip suffices. With the developed algorithms,
we plan to assess children's motor performance in daily life with a follow-up study.
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