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. 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. We performed a systematic search for computer vision-based studies on GMs. 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. 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.

Purpose of Review Substantial research exists focusing on the various aspects and domains of early human development. However, there is a clear blind spot in early postnatal development when dealing with neurodevelopmental disorders, especially those that manifest themselves clinically only in late infancy or even in childhood. Recent Findings This early developmental period may represent an important timeframe to study these disorders but has historically received far less research attention. We believe that only a comprehensive interdisciplinary approach will enable us to detect and delineate specific parameters for specific neurodevelopmental disorders at a very early age to improve early detection/diagnosis, enable prospective studies and eventually facilitate randomised trials of early intervention. Summary In this article, we propose a dynamic framework for characterising neurofunctional biomarkers associated with specific disorders in the development of infants and children. We have named this automated detection ‘Fingerprint Model’, suggesting one possible approach to accurately and early identify neurodevelopmental disorders.

We delineated the achievement of early speech-language milestones in 15 young children with Rett syndrome (MECP2 positive) in the first two years of life using retrospective video analysis. By contrast to the commonly accepted concept that these children are normal in the pre-regression period, we found markedly atypical development of speech-language capacities, suggesting a paradigm shift in the pathogenesis of Rett syndrome and a possible approach to its early detection.

Purpose of Review To summarize findings about the emergence and characteristics of canonical babbling in children with late detected developmental disorders (LDDDs), such as autism spectrum disorder, Rett syndrome, and fragile X syndrome. In particular, we ask whether infants’ vocal development in the first year of life contains any markers that may contribute to earlier detection of these disorders. Recent Findings Only a handful studies have investigated canonical babbling in infants with LDDDs. With divergent research paradigms and definitions applied, findings on the onset and characteristics of canonical babbling are inconsistent and difficult to compare. Infants with LDDDs showed reduced likelihood to produce canonical babbling vocalizations. If achieved, this milestone was more likely to be reached beyond the critical time window of 5–10 months. Summary Canonical babbling appears promising as a potential marker for early detection of infants at risk for developmental disorders. In-depth studies on babbling characteristics in LDDDs are warranted.

Naturalistic Developmental Behavioral Interventions (NDBI) have been evaluated as the most promising interventions for children with autism spectrum disorder. In recent years, a growing body of literature suggests that technological advancements such as Virtual Reality (VR) are promising intervention tools. However, to the best of our knowledge no studies have combined evidence-based practice with such tools. This article aims to review the current literature combining NDBI and VR, and provide suggestions on merging NDBI-approaches with VR. This article is divided into two parts, where we first conduct a review mapping the research applying NDBI-approaches in VR. In the second part we argue how to apply the common features of NDBI into VR-technology. Our findings show that no VR-studies explicitly rely on NDBI-approaches, but some utilize elements in their interventions that are considered to be common features to NDBI. As the results show, to date, no VR-based studies have utilized NDBI in their intervention. We therefore, in the second part of this article, suggests ways to merge VR and NDBI and introduce the term Virtual Naturalistic Developmental Behavioral Interventions (VNDBI). VNDBI is an innovative way of implementing NDBI which will contribute in making interventions more accessible in central as well as remote locations, while reducing unwanted variation between service sites. VNDBI will advance the possibilities of individually tailoring and widen the area of interventions. In addition, VNDBI can provide the field with new knowledge on effective components enhancing the accuracy in the intervention packages and thus move forward the research field and clinical practice.