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 ef...
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| Published in: | Communications medicine Vol. 2; no. 1; p. 69 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
15-06-2022
Nature Portfolio |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | 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.
Plain language summary
Assessment of an infant’s motor abilities is a key part of regular health checks of infant development. However, there is shortage of methods that would allow objective and user-friendly tracking of infant motor abilities. We describe a system that measures infant’s posture and movement with sensors that are attached to the clothing. Movement signals are analyzed with a deep learning algorithm to predict maturity of motor abilities. The accuracy of analysis is comparable to human assessments. This system could enable early diagnosis of developmental delays, and it can be used to assess motor development in clinical trials.
Airaksinen et al. describe an infant wearable system that accurately quantifies key aspects of infant motor ability and uses deep learning algorithms to analyze movement signals. Motor ability age and maturation can be predicted, with the predictions correlating with other clinical and parental assessments. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 2730-664X 2730-664X |
| DOI: | 10.1038/s43856-022-00131-6 |