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

## TL;DR

This paper introduces an automated general movement assessment system using wearable accelerometers and a novel data analysis method, aiming to improve early detection of perinatal stroke in infants at scale.

## Contribution

It presents a new automated GMA approach with Discriminative Pattern Discovery, effective even with coarse data annotations, for early perinatal stroke screening.

## Key findings

- Achieved at least 75% accuracy in recognizing abnormal infant movements.
- Demonstrated effectiveness with a study of 34 newborns, including at-risk infants.
- Supports scalable, automated screening to complement clinical assessments.

## Abstract

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.