Working towards an objective segmental assessment of trunk control in children with cerebral palsy

Trunk Control Measurement Scale (TCMS): Psychometric Properties of Cross-Cultural Adaptation and Validation of the Spanish Version

The aim of this study was to develop a Spanish Version of the Trunk Measurement Scale (TCMS-S) to analyze its validity and reliability and determine the Standard Error of Measurement (SEM) and Minimal Detectable Change (MDC) in children with Cerebral Palsy (CP). Participants were assessed twice 7-15 days apart with the TCMS-S and once with the Gross Motor Function Measurement-88 (GMFM-88), Pediatric Disability Inventory-Computer Adaptive Test (PEDI-CAT), Cerebral Palsy Quality of Life (CPQoL), and Gross Motor Classification System (GMFCS). Internal consistency was evaluated using Cronbach's alpha, and the intraclass correlation (ICC) and kappa coefficients were used to investigate the agreement between the assessments. Finally, 96 participants with CP were included. The TCMS-S showed excellent internal consistency (Cronbach's alpha = 0.95 [0.93 to 0.96]); was highly correlated with the GMFM-88 (rho = 0.816) and the "mobility" subscale of the PEDI-CAT (rho = 0.760); showed a moderate correlation with the "feeling about functioning" CPQoL subscale (rho = 0.576); and differentiated between the GMFCS levels. Excellent test-retest agreement was found for the total and subscale scores (ICC ≥ 0.94 [0.89 to 0.97). For the total TCMS-S score, an SEM of 1.86 and an MDC of 5.15 were found. The TCMS-S is a valid and reliable tool for assessing trunk control in children with CP.

Quantification of the development of trunk control in healthy infants using inertial measurement units

Trunk motor control is essential for the proper functioning of the upper extremities and is an important predictor of gait capacity in children with delayed development. Early diagnosis and intervention could increase the trunk motor capabilities in later life, but current tools used to assess the level of trunk motor control are largely subjective and many lack the sensitivity to accurately monitor development and the effects of therapy. Inertial measurement units could yield an objective quantitative assessment that is inexpensive and easy-to-implement. We hypothesized that root mean square of jerk, a proxy for movement smoothness, could be used to distinguish age and thereby presumed motor development. We attached a sensor to the trunks of six young children with no known developmental deficits. Root mean square of jerk decreases with age, up to 24 months, and is correlated to a more established method, i.e., center-of-pressure velocity, as well as other standard inertial measurement unit outputs. This metric therefore shows potential as a method to differentiate trunk motor control levels.

Cluster analysis of impairment measures to inform an evidence-based classification structure in RaceRunning, a new World Para Athletics event for athletes with hypertonia, ataxia or athetosis

RaceRunning enables athletes with limited or no walking ability to propel themselves independently using a three-wheeled frame that has a saddle, handle bars and a chest plate. For RaceRunning to be included as a para athletics event, an evidence-based classification system is required. This study assessed the impact of trunk control and lower limb impairment measures on RaceRunning performance and evaluated whether cluster analysis of these impairment measures produces a valid classification structure for RaceRunning. The Trunk Control Measurement Scale (TCMS), Selective Control Assessment of the Lower Extremity (SCALE), the Australian Spasticity Assessment Scale (ASAS), and knee extension were recorded for 26 RaceRunning athletes. Thirteen male and 13 female athletes aged 24 (SD = 7) years participated. All impairment measures were significantly correlated with performance (rho = 0.55-0.74). Using ASAS, SCALE, TCMS and knee extension as cluster variables in a two-step cluster analysis resulted in two clusters of athletes. Race speed and the impairment measures were significantly different between the clusters (p < 0.001). The findings of this study provide evidence for the utility of the selected impairment measures in an evidence-based classification system for RaceRunning athletes.

Fully automated image-based estimation of postural point-features in children with cerebral palsy using deep learning

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 (F 1 > 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 F 1 = 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.