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Updated: Jun 17, 2025
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
Published on: May 17, 2024
Associating neuromotor outcomes at 12 months with wearable sensor measures collected during early infancy in rural
Jinseok Oh1, Eva Leticia Tuiz Ordoñez2, Elisa Velasquez3
1Division of Developmental-Behavioral Pediatrics, Children's Hospital Los Angeles, USA.
Insights
Fuzzy entropy in infant leg movements, measured by wearable sensors, can predict motor development outcomes. This new technique offers a sensitive measure for early infant neuromotor prediction.
Area of Science:
- Infant Development
- Wearable Sensor Technology
- Neuromotor Outcome Prediction
Background:
- Sensitive measures to predict infant neuromotor outcomes are currently lacking.
- Wearable sensors offer a potential new method for collecting infant movement data.
- Early prediction of developmental trajectories is crucial for timely interventions.
Purpose of the Study:
- To investigate the predictive value of leg movement characteristics for infant developmental outcomes.
- To assess if wearable sensor data can identify early predictors of neuromotor development.
- To explore the association between movement complexity and later developmental scores.
Main Methods:
- Collected full-day leg movement data from 41 infants using wearable sensors between birth and 6 months.
- Derived measures including average leg movement rate and fuzzy entropy from peak acceleration time series.
- Utilized quantile regressions to test the predictability of Bayley Scales of Infant and Toddler Development III scores at 12 months.
Main Results:
- Fuzzy entropy of leg movements was significantly associated with the motor composite score at 0.5 quantiles.
- No significant associations were found between leg movement characteristics and language or cognitive composite scores.
- The study identified fuzzy entropy as a potential predictor for motor development.
Conclusions:
- The entropy of leg movement, derived from wearable sensor data, can predict infant motor developmental outcomes.
- This method provides a novel, sensitive approach for assessing neuromotor development in early infancy.
- Wearable sensor-based movement analysis shows promise for early identification of motor delays.
Background:
Sensitive measures to predict neuromotor outcomes from data collected early in infancy are lacking. Measures derived from the recordings of infant movement using wearable sensors may be a useful new technique.
Methods:
We collected full-day leg movement of 41 infants in rural Guatemala across 3 visits between birth and 6 months of age using wearable sensors. Average leg movement rate and fuzzy entropy, a measure to describe the complexity of signals, of the leg movements' peak acceleration time series and the time series itself were derived. We tested the three measures for the predictability of infants' developmental outcome, Bayley Scales of Infant and Toddler Development III motor, language, or cognitive composite score assessed at 12 months of age. We performed quantile regressions with clustered standard errors, accounting for the multiple visits for each infant.
Results:
Fuzzy entropy was associated with the motor composite score at the 0.5 quantiles; this association was not found for the other two measures. Also, no leg movement characteristic was associated with language or cognitive composite scores.
Conclusion:
We propose that the entropy of leg movement associated peak accelerations calculated from the wearable sensor data collected for a full-day can be considered as one predictor for infants' motor developmental outcome assessed with Bayley Scales of Infant and Toddler Development III at 12 months of age.
