Abstract
The aim of this study was to provide automated identification of postural point-features required to estimate the location and orientation of the head, multi-segmented trunk and arms from videos of the clinical test ‘Segmental Assessment of Trunk Control’ (SATCo). Three expert operators manually annotated 13 point-features in every fourth image of 177 short (5–10 s) videos (25 Hz) of 12 children with cerebral palsy (aged: 4.52 ± 2.4 years), participating in SATCo testing. Linear interpolation for the remaining images resulted in 30 825 annotated images. Convolutional neural networks were trained with cross-validation, giving held-out test results for all children. The point-features were estimated with error 4.4 ± 3.8 pixels at approximately 100 images per second. Truncal segment angles (head, neck and six thoraco-lumbar–pelvic segments) were estimated with error 6.4 ± 2.8°, allowing accurate classification (F1 > 80%) of deviation from a reference posture at thresholds up to 3°, 3° and 2°, respectively. Contact between arm point-features (elbow and wrist) and supporting surface was classified at F1 = 80.5%. This study demonstrates, for the first time, technical feasibility to automate the identification of (i) a sitting segmental posture including individual trunk segments, (ii) changes away from that posture, and (iii) support from the upper limb, required for the clinical SATCo.
Highlights
A primary aim of physical therapy for children with neuromotor disability, such as cerebral palsy (CP), is to improve postural control in order to enhance both fine and gross motor skills [1] with postural control of the head and trunk being of primary importance [2]
The classification of neuromuscular control at a given head/trunk segment requires two conditions to be met: (i) ‘alignment’: the segment should remain aligned to the neutral vertical posture within a defined threshold, and (ii) ‘contact’: there should be no contact between the upper limbs and the head/trunk or external surface [8]
The neural networks were trained to minimize the absolute difference between their output and the labels
Summary
A primary aim of physical therapy for children with neuromotor disability, such as cerebral palsy (CP), is to improve postural control in order to enhance both fine and gross motor skills [1] with postural control of the head and trunk being of primary importance [2]. Current clinical physical therapy assessments of controlled sitting ability for children with CP, reliable, infer motor control status from the subjective observation of functional abilities [3,4,5,6]. The Segmental Assessment of Trunk Control (SATCo) [8] addresses these issues, providing a comprehensive assessment of seated postural control. It systematically assesses control of the neutral vertical posture in sitting at six discrete head/trunk segmental levels and free sitting. The classification of neuromuscular control at a given head/trunk segment requires two conditions to be met: (i) ‘alignment’: the segment should remain aligned to the neutral vertical posture within a defined threshold, and (ii) ‘contact’: there should be no contact between the upper limbs and the head/trunk or external surface [8]
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