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

dc.contributorAalto Universityen
dc.contributor.authorAiraksinen, Manu
dc.contributor.authorGallen, Anastasia
dc.contributor.authorKivi, Anna
dc.contributor.authorVijayakrishnan, Pavithra
dc.contributor.authorHäyrinen, Taru
dc.contributor.authorIlen, Elina
dc.contributor.authorRäsänen, Okko
dc.contributor.authorHaataja, Leena
dc.contributor.authorVanhatalo, Sampsa
dc.contributor.departmentBABA Center
dc.contributor.departmentHelsinki University Hospital
dc.contributor.departmentDepartment of Design
dc.contributor.departmentTampere University
dc.contributor.departmentUniversity of Helsinki
dc.description.abstractBackground 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  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.en
dc.description.versionPeer revieweden
dc.identifier.citationAiraksinen , M , Gallen , A , Kivi , A , Vijayakrishnan , P , Häyrinen , T , Ilen , E , Räsänen , O , Haataja , L & Vanhatalo , S 2022 , ' Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants ' , Communications Medicine , vol. 2022 , no. 2 , 69 .
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dc.relation.ispartofseriesCommunications Medicineen
dc.relation.ispartofseriesVolume 2022, issue 2en
dc.titleIntelligent wearable allows out-of-the-lab tracking of developing motor abilities in infantsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi