[Analysis of motor development within the first year of life: 3-D motion tracking without markers for early detection of developmental disorders]. / Analyse der Spontanmotorik im 1. Lebensjahr: Markerlose 3-D-Bewegungserfassung zur Früherkennung von Entwicklungsstörungen.

Children with motor development disorders benefit greatly from early interventions. An early diagnosis in pediatric preventive care (U2-U5) can be improved by automated screening. Current approaches to automated motion analysis, however, are expensive, require lots of technical support, and cannot be used in broad clinical application. Here we present an inexpensive, marker-free video analysis tool (KineMAT) for infants, which digitizes 3­D movements of the entire body over time allowing automated analysis in the future.Three-minute video sequences of spontaneously moving infants were recorded with a commercially available depth-imaging camera and aligned with a virtual infant body model (SMIL model). The virtual image generated allows any measurements to be carried out in 3­D with high precision. We demonstrate seven infants with different diagnoses. A selection of possible movement parameters was quantified and aligned with diagnosis-specific movement characteristics.KineMAT and the SMIL model allow reliable, three-dimensional measurements of spontaneous activity in infants with a very low error rate. Based on machine-learning algorithms, KineMAT can be trained to automatically recognize pathological spontaneous motor skills. It is inexpensive and easy to use and can be developed into a screening tool for preventive care for children.

General Movement Assessment from videos of computed 3D infant body models is equally effective compared to conventional RGB video rating.

BACKGROUND: General Movement Assessment (GMA) is a powerful tool to predict Cerebral Palsy (CP). Yet, GMA requires substantial training challenging its broad implementation in clinical routine. This inspired a world-wide quest for automated GMA. AIMS: To test whether a low-cost, marker-less system for three-dimensional motion capture from RGB depth sequences using a whole body infant model may serve as the basis for automated GMA. STUDY DESIGN: Clinical case study at an academic neurodevelopmental outpatient clinic. SUBJECTS: Twenty-nine high risk infants were assessed at their clinical follow-up at 2-4 month corrected age (CA). Their neurodevelopmental outcome was assessed regularly up to 12-31 months CA. OUTCOME MEASURES: GMA according to Hadders-Algra by a masked GMA-expert of conventional and computed 3D body model ("SMIL motion") videos of the same GMs. Agreement between both GMAs was tested using dichotomous and graded scaling with Kappa and intraclass correlations, respectively. Sensitivity and specificity to predict CP at ≥12 months CA were assessed. RESULTS: Agreement of the two GMA ratings was moderate-good for GM-complexity (κ = 0.58; ICC = 0.874 [95%CI 0.730; 0.941]) and substantial-good for fidgety movements (FMs; Kappa = 0.78, ICC = 0.926 [95%CI 0.843; 0.965]). Five children were diagnosed with CP (four bilateral, one unilateral CP). The GMs of the child with unilateral CP were twice rated as mildly abnormal with FMs. GM-complexity and somewhat less FMs, of both conventional and SMIL motion videos predicted bilateral CP comparably to published literature. CONCLUSIONS: Our computed infant 3D full body model is an attractive starting point for automated GMA in infants at risk of CP.