Gao, Yan and Long, Yang and Guan, Yu and Basu, Anna and Baggaley, Jessica and Ploetz, Thomas (2019) 'Towards reliable, automated general movement assessment for perinatal stroke screening in infants using wearable accelerometers.', Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies., 3 (1). p. 12.
Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to life-long disability, in particular Cerebral Palsy (CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be used to classify infant movements using a Gestalt approach, identifying infants at high risk of developing PS. Training and maintenance of assessment skills are essential and expensive for the correct use of GMA, yet many practitioners lack these skills, preventing larger-scale screening and leading to significant risks of missing opportunities for early detection and intervention for affected infants. We present an automated approach to GMA, based on body-worn accelerometers and a novel sensor data analysis method--Discriminative Pattern Discovery (DPD)--that is designed to cope with scenarios where only coarse annotations of data are available for model training. We demonstrate the effectiveness of our approach in a study with 34 newborns (21 typically developing infants and 13 PS infants with abnormal movements). Our method is able to correctly recognise the trials with abnormal movements with at least the accuracy that is required by newly trained human annotators (75%), which is encouraging towards our ultimate goal of an automated PS screening system that can be used population-wide.
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1145/3314399|
|Publisher statement:||© Owner/Author | ACM 2019. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies}, https://doi.org/10.1145/3314399|
|Date accepted:||31 January 2019|
|Date deposited:||02 September 2019|
|Date of first online publication:||29 March 2019|
|Date first made open access:||02 September 2019|
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