TY - JOUR
T1 - The future of General Movement Assessment
T2 - The role of computer vision and machine learning - A scoping review
AU - Silva, Nelson
AU - Zhang, Dajie
AU - Kulvicius, Tomas
AU - Gail, Alexander
AU - Barreiros, Carla
AU - Lindstaedt, Stefanie
AU - Kraft, Marc
AU - Bölte, Sven
AU - Poustka, Luise
AU - Nielsen-Saines, Karin
AU - Wörgötter, Florentin
AU - Einspieler, Christa
AU - Marschik, Peter B
N1 - Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.
PY - 2021/3
Y1 - 2021/3
N2 - BACKGROUND: The clinical and scientific value of Prechtl general movement assessment (GMA) has been increasingly recognised, which has extended beyond the detection of cerebral palsy throughout the years. With advancing computer science, a surging interest in developing automated GMA emerges.AIMS: In this scoping review, we focused on video-based approaches, since it remains authentic to the non-intrusive principle of the classic GMA. Specifically, we aimed to provide an overview of recent video-based approaches targeting GMs; identify their techniques for movement detection and classification; examine if the technological solutions conform to the fundamental concepts of GMA; and discuss the challenges of developing automated GMA.METHODS AND PROCEDURES: We performed a systematic search for computer vision-based studies on GMs.OUTCOMES AND RESULTS: We identified 40 peer-reviewed articles, most (n = 30) were published between 2017 and 2020. A wide variety of sensing, tracking, detection, and classification tools for computer vision-based GMA were found. Only a small portion of these studies applied deep learning approaches. A comprehensive comparison between data acquisition and sensing setups across the reviewed studies, highlighting limitations and advantages of each modality in performing automated GMA is provided.CONCLUSIONS AND IMPLICATIONS: A "method-of-choice" for automated GMA does not exist. Besides creating large datasets, understanding the fundamental concepts and prerequisites of GMA is necessary for developing automated solutions. Future research shall look beyond the narrow field of detecting cerebral palsy and open up to the full potential of applying GMA to enable an even broader application.
AB - BACKGROUND: The clinical and scientific value of Prechtl general movement assessment (GMA) has been increasingly recognised, which has extended beyond the detection of cerebral palsy throughout the years. With advancing computer science, a surging interest in developing automated GMA emerges.AIMS: In this scoping review, we focused on video-based approaches, since it remains authentic to the non-intrusive principle of the classic GMA. Specifically, we aimed to provide an overview of recent video-based approaches targeting GMs; identify their techniques for movement detection and classification; examine if the technological solutions conform to the fundamental concepts of GMA; and discuss the challenges of developing automated GMA.METHODS AND PROCEDURES: We performed a systematic search for computer vision-based studies on GMs.OUTCOMES AND RESULTS: We identified 40 peer-reviewed articles, most (n = 30) were published between 2017 and 2020. A wide variety of sensing, tracking, detection, and classification tools for computer vision-based GMA were found. Only a small portion of these studies applied deep learning approaches. A comprehensive comparison between data acquisition and sensing setups across the reviewed studies, highlighting limitations and advantages of each modality in performing automated GMA is provided.CONCLUSIONS AND IMPLICATIONS: A "method-of-choice" for automated GMA does not exist. Besides creating large datasets, understanding the fundamental concepts and prerequisites of GMA is necessary for developing automated solutions. Future research shall look beyond the narrow field of detecting cerebral palsy and open up to the full potential of applying GMA to enable an even broader application.
KW - Augmented general movement assessment
KW - Automation
KW - Cerebral palsy
KW - Computer vision
KW - Deep learning
KW - Developmental disorder
KW - Early detection
KW - General movements
KW - Infancy
KW - Machine learning
KW - Neurodevelopment
KW - Pose estimation
UR - http://www.scopus.com/inward/record.url?scp=85101461649&partnerID=8YFLogxK
U2 - 10.1016/j.ridd.2021.103854
DO - 10.1016/j.ridd.2021.103854
M3 - Article
C2 - 33571849
SN - 0891-4222
VL - 110
JO - Research in Developmental Disabilities
JF - Research in Developmental Disabilities
M1 - 103854
ER -