Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants.

Background: Early neurodevelopmental care needs better, effective and objective solutions for assessing infants' motor abilities. Novel wearable technology opens possibilities for characterizing spontaneous movement behavior. This work seeks to construct and validate a generalizable, scalable, and effective method to measure infants' spontaneous motor abilities across all motor milestones from lying supine to fluent walking. Methods: A multi-sensor infant wearable was constructed, and 59 infants (age 5-19 months) were recorded during their spontaneous play. A novel gross motor description scheme was used for human visual classification of postures and movements at a second-level time resolution. A deep learning -based classifier was then trained to mimic human annotations, and aggregated recording-level outputs were used to provide posture- and movement-specific developmental trajectories, which enabled more holistic assessments of motor maturity. Results: Recordings were technically successful in all infants, and the algorithmic analysis showed human-equivalent-level accuracy in quantifying the observed postures and movements. The aggregated recordings were used to train an algorithm for predicting a novel neurodevelopmental measure, Baba Infant Motor Score (BIMS). This index estimates maturity of infants' motor abilities, and it correlates very strongly (Pearson's r = 0.89, p < 1e-20) to the chronological age of the infant. Conclusions: The results show that out-of-hospital assessment of infants' motor ability is possible using a multi-sensor wearable. The algorithmic analysis provides metrics of motility that are transparent, objective, intuitively interpretable, and they link strongly to infants' age. Such a solution could be automated and scaled to a global extent, holding promise for functional benchmarking in individualized patient care or early intervention trials.

Profile of minor neurological findings after perinatal asphyxia.

AIM: To characterise the spectrum of findings in sequential neurological examinations, general movements (GM) assessment and magnetic resonance imaging (MRI) of infants with perinatal asphyxia. METHODS: The prospective cohort study of term infants with perinatal asphyxia treated at Helsinki University Hospital's neonatal units in 2016-2020 used Hammersmith Neonatal Neurological Examination (HNNE) and brain MRI at 2 weeks and Hammersmith Infant Neurological Examination (HINE) and GM assessment at 3 months of age. RESULTS: Analysis included 50 infants: 33 displaying perinatal asphyxia without hypoxic-ischaemic encephalopathy (HIE), seven with HIE1 and 10 with HIE2. Of the infants with atypical HNNE findings, 24/25 perinatal asphyxia without HIE cases, 5/6 HIE1 cases and all 10 HIE2 cases showed atypical findings in the HINE. The HINE identified atypical spontaneous movements significantly more often in infants with white matter T2 hyperintensity. CONCLUSION: In this cohort, most infants with perinatal asphyxia, with or without HIE, presented atypical neurological findings in sequential examinations. The profile of neurological findings for children with perinatal asphyxia without HIE resembled that of children with HIE. White matter T2 hyperintensity was associated with atypical spontaneous movements in the HINE and was a frequent MRI finding also in perinatal asphyxia without HIE.

Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors.

Infants' spontaneous and voluntary movements mirror developmental integrity of brain networks since they require coordinated activation of multiple sites in the central nervous system. Accordingly, early detection of infants with atypical motor development holds promise for recognizing those infants who are at risk for a wide range of neurodevelopmental disorders (e.g., cerebral palsy, autism spectrum disorders). Previously, novel wearable technology has shown promise for offering efficient, scalable and automated methods for movement assessment in adults. Here, we describe the development of an infant wearable, a multi-sensor smart jumpsuit that allows mobile accelerometer and gyroscope data collection during movements. Using this suit, we first recorded play sessions of 22 typically developing infants of approximately 7 months of age. These data were manually annotated for infant posture and movement based on video recordings of the sessions, and using a novel annotation scheme specifically designed to assess the overall movement pattern of infants in the given age group. A machine learning algorithm, based on deep convolutional neural networks (CNNs) was then trained for automatic detection of posture and movement classes using the data and annotations. Our experiments show that the setup can be used for quantitative tracking of infant movement activities with a human equivalent accuracy, i.e., it meets the human inter-rater agreement levels in infant posture and movement classification. We also quantify the ambiguity of human observers in analyzing infant movements, and propose a method for utilizing this uncertainty for performance improvements in training of the automated classifier. Comparison of different sensor configurations also shows that four-limb recording leads to the best performance in posture and movement classification.