Cerebral palsy (CP) is a non-progressive disorder resulting from injuries or
abnormalities in fetal or early infant brain development. According to registries from
European countries, the condition affects 2-3 out of every 1000 live births. An
individual with CP typically presents with motor development disorders that cause
abnormal patterns of movement and posture due to impaired coordination of movements and
muscle tone regulation. People with cerebral palsy can also have various other problems,
including sensory and cognitive problems and sleep disturbances. These symptoms result in
limitations in activity level and societal participation throughout the individual's
life. Adolescents and even children as young as seven may experience a decline in motor
ability, leading to changes in their movement behavior. Healthcare professionals rely on
various observations and measurements performed in clinical and hospital settings to
assess and treat individuals with CP. However, there is some uncertainty about whether
these assessments truly reflect real-life movement behaviors, as using an impaired
extremity in everyday life frequently deviates from its motor capacity. There is an
absence of robust tools that capture daytime and nighttime movement behavior in
real-world settings rather than in clinical or controlled environments. Hemiparesis is
the most common marker of CP, making asymmetrical deficits a target for intensive
interventions such as physical and occupational therapy. Yet, no clinical tools are
available that document asymmetrical differences in the real world in children and
adolescents with CP. An objective method to measure real-world movement patterns would
allow therapists to identify individuals who need a more comprehensive evaluation and to
target interventions and other management strategies more precisely. This would help
children and adolescents with CP gain motor skills to maximize independence. Further,
objectively observing individuals with CP in their daily lives is essential to gain
insights into functional decline. It has been observed that children and adolescents with
CP are more likely to experience sleep-related difficulties such as difficulty initiating
sleep, frequent nocturnal awakenings, discomfort while in bed, and early morning
awakenings. As sleep quality plays a vital role in health-related quality of life, it is
crucial to have objective methods to evaluate and monitor potential sleep problems in a
real-world context.
A deep-learning convolutional neural network has been modeled to recognize postures
lying, sitting, and standing the activity of walking, and movements of the right and left
extremities. The network uses accelerometer and gyroscope data from 7 wearable sensors.
Testing of the network´s performance found that it surpasses human annotators in
accurately classifying the movement behavior of healthy and typically developed adults.
These findings are currently under review and have yet to be published. The present
protocol details the methodology for assessing the feasibility of real-world movement
behavior monitoring and the discriminant validity of the network in adolescents with CP
and controls.
The feasibility evaluation examines the technology used, e.g., potential data loss and
the credibility of data output, as well as user acceptance, e.g., sensor wear time and
adverse events. The networks' discriminant ability will be assessed by the network's
ability to differentiate between controls and CP severity, e.g., scores on the Gross
Motor Functional Classification Scale - Expanded and revised (GMFCS-E&R), different types
of CP, differently affected body parts of the participating adolescents with CP, as well
as individuals who have and have not sleep problems in the entire cohort.
