Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors

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dc.contributor University of Helsinki, University of Helsinki en
dc.contributor University of Helsinki, HUS Children and Adolescents en
dc.contributor University of Helsinki, University of Helsinki en
dc.contributor University of Helsinki, Children's Hospital en
dc.contributor University of Helsinki, University of Helsinki en
dc.contributor University of Helsinki, HUS Medical Imaging Center en
dc.contributor.author Airaksinen, Manu
dc.contributor.author Räsänen, Okko
dc.contributor.author Ilen, Elina
dc.contributor.author Häyrinen, Taru
dc.contributor.author Kivi, Anna
dc.contributor.author Marchi, Viviana
dc.contributor.author Gallen, Anastasia
dc.contributor.author Blom, Sonja
dc.contributor.author Varhe, Anni
dc.contributor.author Kaartinen, Nico
dc.contributor.author Haataja, Leena
dc.contributor.author Vanhatalo, Sampsa
dc.date.accessioned 2020-03-12T13:34:01Z
dc.date.available 2020-03-12T13:34:01Z
dc.date.issued 2020-01-13
dc.identifier.citation Airaksinen , M , Räsänen , O , Ilen , E , Häyrinen , T , Kivi , A , Marchi , V , Gallen , A , Blom , S , Varhe , A , Kaartinen , N , Haataja , L & Vanhatalo , S 2020 , ' Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors ' , Scientific Reports , vol. 10 , no. 1 , 169 . https://doi.org/10.1038/s41598-019-56862-5 en
dc.identifier.issn 2045-2322
dc.identifier.other PURE: 133547567
dc.identifier.other PURE UUID: 44dfbff3-259f-4f1c-a496-663ee095985e
dc.identifier.other WOS: 000511157300014
dc.identifier.other ORCID: /0000-0002-8057-6194/work/70953491
dc.identifier.uri http://hdl.handle.net/10138/313235
dc.description.abstract Infants' spontaneous and voluntary movements mirror developmental integrity of brain networks since they require coordinated activation of multiple sites in the central nervous system. Accordingly, early detection of infants with atypical motor development holds promise for recognizing those infants who are at risk for a wide range of neurodevelopmental disorders (e.g., cerebral palsy, autism spectrum disorders). Previously, novel wearable technology has shown promise for offering efficient, scalable and automated methods for movement assessment in adults. Here, we describe the development of an infant wearable, a multi-sensor smart jumpsuit that allows mobile accelerometer and gyroscope data collection during movements. Using this suit, we first recorded play sessions of 22 typically developing infants of approximately 7 months of age. These data were manually annotated for infant posture and movement based on video recordings of the sessions, and using a novel annotation scheme specifically designed to assess the overall movement pattern of infants in the given age group. A machine learning algorithm, based on deep convolutional neural networks (CNNs) was then trained for automatic detection of posture and movement classes using the data and annotations. Our experiments show that the setup can be used for quantitative tracking of infant movement activities with a human equivalent accuracy, i.e., it meets the human inter-rater agreement levels in infant posture and movement classification. We also quantify the ambiguity of human observers in analyzing infant movements, and propose a method for utilizing this uncertainty for performance improvements in training of the automated classifier. Comparison of different sensor configurations also shows that four-limb recording leads to the best performance in posture and movement classification. en
dc.format.extent 13
dc.language.iso eng
dc.relation.ispartof Scientific Reports
dc.rights en
dc.subject GENERAL MOVEMENTS en
dc.subject EARLY INTERVENTION en
dc.subject PRETERM en
dc.subject TERM en
dc.subject 3123 Gynaecology and paediatrics en
dc.title Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.1038/s41598-019-56862-5
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion
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