SummaryBackgroundEarly neurodevelopmental care and research are in urgent need of practical methods for quantitative assessment of early motor development. Here, performance of a wearable system in early motor assessment was validated and compared to developmental tracking of physical growth charts.MethodsAltogether 1358 h of spontaneous movement during 226 recording sessions in 116 infants (age 4–19 months) were analysed using a multisensor wearable system. A deep learning-based automatic pipeline quantified categories of infants' postures and movements at a time scale of seconds. Results from an archived cohort (dataset 1, N = 55 infants) recorded under partial supervision were compared to a validation cohort (dataset 2, N = 61) recorded at infants’ homes by the parents. Aggregated recording-level measures including developmental age prediction (DAP) were used for comparison between cohorts. The motor growth was also compared with respective DAP estimates based on physical growth data (length, weight, and head circumference) obtained from a large cohort (N = 17,838 infants; age 4–18 months).FindingsAge-specific distributions of posture and movement categories were highly similar between infant cohorts. The DAP scores correlated tightly with age, explaining 97–99% (94–99% CI 95) of the variance at the group average level, and 80–82% (72–88%) of the variance in the individual recordings. Both the average motor and the physical growth measures showed a very strong fit to their respective developmental models (R2= 0.99). However, single measurements showed more modality-dependent variation that was lowest for motor (σ= 1.4 [1.3–1.5 CI 95] months), length (σ= 1.5 months), and combined physical (σ= 1.5 months) measurements, and it was clearly higher for the weight (σ= 1.9 months) and head circumference (σ= 1.9 months) measurements. Longitudinal tracking showed clear individual trajectories, and its accuracy was comparable between motor and physical measures with longer measurement intervals.InterpretationA quantified, transparent and explainable assessment of infants' motor performance is possible with a fully automated analysis pipeline, and the results replicate across independent cohorts from out-of-hospital recordings. A holistic assessment of motor development provides an accuracy that is comparable with the conventional physical growth measures. A quantitative measure of infants’ motor development may directly support individual diagnostics and care, as well as facilitate clinical research as an outcome measure in early intervention trials.FundingThis work was supported by theFinnish Academy(314602, 335788, 335872, 332017, 343498),Finnish Pediatric Foundation(Lastentautiensäätiö),Aivosäätiö,10.13039/501100006306Sigrid Jusélius Foundation, andHUS Children’s Hospital/HUS diagnostic center research funds.