Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable.

Developing objective and quantitative methods of early gross motor assessment is essential to better understand neurodevelopment and to support early therapeutic interventions. Here, we present a method to quantify gross motor performance using a multisensor wearable, MAIJU (Motility Assessment of Infants with a JUmpsuit), which offers an automated, scalable, quantitative, and objective assessment using a fully automated cloud-based pipeline. This wearable suit is equipped with four movement sensors that record synchronized data to a mobile phone utilizing a low-energy Bluetooth connection. An offline analysis in the cloud server generates fully analyzed results within minutes for each recording. These results include a graphical report of the recording session and a detailed result matrix that gives second-by-second classifications for posture, movement, infant carrying, and free playtime. Our recent results show the virtue of such quantified motor assessment providing a potentially effective method for distinguishing variations in the infant's gross motor development.

A growth chart of brain function from infancy to adolescence based on EEG.

BACKGROUND: In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function. METHODS: We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states). FINDINGS: The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028). INTERPRETATION: A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts. FUNDING: This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation.