عنوان انگلیسی مقاله:
The future of General Movement Assessment: The role of computer vision and machine learning – A scoping review
ترجمه فارسی عنوان مقاله:
آینده ارزیابی جنبش عمومی: نقش بینایی رایانه و یادگیری ماشین - مروری بر محدوده
Sciencedirect - Elsevier - Research in Developmental Disabilities, 110 (2021) 103854: doi:10:1016/j:ridd:2021:103854
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
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
Keywords: Augmented general movement assessment | Automation | Cerebral palsy | Computer vision | Deep learning | Developmental disorder | Early detection | General movements | Infancy | Machine learning | Neurodevelopment | Pose estimation