Abstract
Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the baby’s movement, even in cribs. Traditional technologies for automatic baby motion analysis leverage contact sensors. Alternatively, remotely acquired video data (e.g., RGB or depth) can be used, with or without active/passive markers positioned on the body. Markerless approaches are easier to set up and maintain (without any human intervention) and they work well on non-collaborative users, making them the most suitable technologies for clinical applications involving children. On the other hand, they require complex computational strategies for extracting knowledge from data, and then, they strongly depend on advances in computer vision and machine learning, which are among the most expanding areas of research. As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview of how recent scientific developments impacted it. This paper tries to fill this gap and it lists specifically designed data acquisition tools and publicly available datasets as well. Besides, it gives a glimpse of the most promising techniques in computer vision, machine learning and pattern recognition which could be profitably exploited for children motion analysis in videos.
Highlights
Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system
This paper summarizes the most relevant works on movement analysis in young children employing mainly machine learning techniques and starting from image/video data
A few works concentrated on baby motion analysis from input video data and they collected only papers dealing with general movement assessment (GMA) issues
Summary
Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. The aforementioned survey papers are very interesting but they gave a task-specific (e.g., GMA or ASD assessment) view of the exploited computer vision and machine learning methods This makes it difficult to understand how proposed approaches can be transferred to other tasks, involving different disorders and ages. A more global and structured vision of the problem would certainly be desirable to incentive research and its applicative effects but, to the best of our knowledge, the literature lacks a manuscript giving a broader overview of the video-based analysis of children motion for assessment of NDD This paper fills this gap by providing a survey on advanced computational methods for early NDD diagnosis starting from temporal sequences of 2D/3D data.
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