Amazing stuff! However, it appears this research is not quite new, see e.g. A smart jumpsuit tracks infants’ motor development (published in 2022 by the same institution as the senior author of this research article)
From the editor's summary and abstract:
"Editor’s summary
Evaluation of infant spontaneous motor activity currently requires clinic visits and trained observers.
Airaksinen et al. used a wearable motion sensing jumpsuit to monitor infant movement and postures at home during free play.
Deep-learning classifiers objectively characterized 220 motor metrics in 92 typically developing infants aged 4 to 19 months, generating motor growth charts of emerging postures and their dynamics.
Approximately 91% of these motor metrics generalized to an independent clinical cohort of infants including both typical and atypical neurodevelopment, and 55% of the metrics statistically distinguished the two groups.
These findings suggest that wearable sensors could provide objective, home based quantitative measures of motor development dynamics to support routine care or to better understand developmental patterns. ...
Abstract
Early gross motor performance is a key component of neurodevelopmental assessments, and longitudinal follow-ups in personalized health care need better objective methods for measuring it. We studied how the detailed characteristics of infants’ gross motor development could be modeled with age-normative growth charts. We performed serial at-home measurements (total of 580 measurements, 1227 hours of free playtime) from a cohort of 92 typically developing infants at 4 to 19 months of age, using wearable suits with four movement sensors. A previously developed and validated, fully automated analysis pipeline detected postures and movements at a second-by-second resolution, to be used for deriving 220 detailed motor metrics of postures, movements, transition dynamics, locomotion types, and activity counts. The results showed that early gross motor development is sufficiently stable for constructing robust growth charts, most prominently for modeling evolving postures and their dynamics, and a wide range of motor metrics have sufficient temporal stability to provide reliable individual-level tracking. External validation in a clinical cohort with 39 infants, including participants with both typical and abnormal neurodevelopment, showed that the growth charts were highly generalizable (91% of the studied motor metrics were statistically comparable to those of the normative cohort), and 55% of the motor metrics showed statistical differentiation of abnormal neurodevelopment. These findings indicate that measurements of at-home wearables can provide assessments to meet a wide range of needs in health care and developmental science."
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