Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants

Manu Airaksinen; Anastasia Gallen; Anna Kivi; Pavithra Vijayakrishnan; Taru Häyrinen; Elina Ilén; Okko Räsänen; Leena M Haataja; Sampsa Vanhatalo

doi:10.1038/s43856-022-00131-6

Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants

Commun Med (Lond). 2022 Jun 15:2:69. doi: 10.1038/s43856-022-00131-6. eCollection 2022.

Authors

Manu Airaksinen¹, Anastasia Gallen¹, Anna Kivi^{1

2}, Pavithra Vijayakrishnan¹, Taru Häyrinen^{1

2}, Elina Ilén³, Okko Räsänen⁴, Leena M Haataja^{1

2}, Sampsa Vanhatalo^{1

5}

Affiliations

¹ BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children's Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland.
² Department of Pediatric Neurology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
³ Department of Design, Aalto University, Otaniementie 14, FI-02150 Espoo, Finland.
⁴ Unit of Computing Sciences, Tampere University, P.O. Box 553, FI-33101 Tampere, Finland.
⁵ Department of Physiology, University of Helsinki, Helsinki, Finland.

Abstract

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.

Keywords: Biomarkers; Paediatric research.