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Perinatal stroke (PS) is a serious condition that, if undetected and thusuntreated, often leads to life-long disability, in particular Cerebral Palsy(CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can beused to classify infant movements using a Gestalt approach, identifying infantsat high risk of developing PS. Training and maintenance of assessment skillsare essential and expensive for the correct use of GMA, yet many practitionerslack these skills, preventing larger-scale screening and leading to significantrisks of missing opportunities for early detection and intervention foraffected infants. We present an automated approach to GMA, based on body-wornaccelerometers and a novel sensor data analysis method-Discriminative PatternDiscovery (DPD)-that is designed to cope with scenarios where only coarseannotations of data are available for model training. We demonstrate theeffectiveness of our approach in a study with 34 newborns (21 typicallydeveloping infants and 13 PS infants with abnormal movements). Our method isable to correctly recognise the trials with abnormal movements with at leastthe accuracy that is required by newly trained human annotators (75%), which isencouraging towards our ultimate goal of an automated PS screening system thatcan be used population-wide.

Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers