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

arXiv: https://arxiv.org/abs/1902.08068 · HTML: https://mlsanity.com/paper/1902.08068/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2019.

## Built on by (most cited first, 8 of 8)

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition](https://mlsanity.com/paper/2202.07260/) (2022)
- [ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition](https://mlsanity.com/paper/2305.13541/) (2023)
- [Activity-Aware Deep Cognitive Fatigue Assessment using Wearables](https://mlsanity.com/paper/2105.02824/) (2021)
- [Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2212.13918/) (2022)
- [The Blind Spot in 2D Infants' Pose Estimation:Robust Learning from Noisy Annotations](https://mlsanity.com/paper/2609.04009/) (2026)
- [Knowing the Past to Predict the Future: Reinforcement Virtual Learning](https://mlsanity.com/paper/2211.01266/) (2022)
- [MMA: A Momentum Mamba Architecture for Human Activity Recognition with Inertial Sensors](https://mlsanity.com/paper/2511.21550/) (2025)

## Abstract (arXiv)

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.