Open Video Data Sharing in Developmental and Behavioural Science

Marschik, Peter B.; Kulvičius, Tomas; Flügge, Sarah; Widmann, Claudius A. A.; Saines, Karin Nielsen; Schulte‐Rüther, Martin; Hüning, Britta; Bölte, Sven; Poustka, Luise; Sigafoos, Jeff; Wörgötter, Florentin; Einspieler, Christa; Zhang, Dajie

doi:10.2139/ssrn.4231648

Cited by 3 publications

(1 citation statement)

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“…Over the past few decades, not only have technological possibilities for the automated classification of general movements evolved and artificial intelligence and machine learning been increasingly applied [6,[22][23][24][25][26], but also the clinical knowledge on general movement patterns have become more differentiated and fine-grained [17,27]. Especially with the MOS-R, the extension of the classic GMA, as assessment tool, a variety of studies have described the spontaneous motor patterns of infants with divergent medical backgrounds [27].…”

Section: Discussionmentioning

confidence: 99%

Building Blocks for Deep Phenotyping in Infancy: A Use Case Comparing Spontaneous Neuromotor Functions in Prader-Willi Syndrome and Cerebral Palsy

Zhang

Wang

Shen

et al. 2023

JCM

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With the increasing worldwide application of the Prechtl general movements assessment (GMA) beyond its original field of the early prediction of cerebral palsy (CP), substantial knowledge has been gained on early neuromotor repertoires across a broad spectrum of diagnostic groups. Here, we aimed to profile the neuromotor functions of infants with Prader-Willi syndrome (PWS) and to compare them with two other matched groups. One group included infants with CP; the other included patients who were treated at the same clinic and turned out to have inconspicuous developmental outcomes (IOs). The detailed GMA, i.e., the motor optimality score-revised (MOS-R), was used to prospectively assess the infants’ (N = 54) movements. We underwent cross-condition comparisons to characterise both within-group similarities and variations and between-group distinctions and overlaps in infants’ neuromotor functions. Although infants in both the PWS and the CP groups scored similarly low on MOS-R, their motor patterns were different. Frog-leg and mantis-hand postures were frequently seen in the PWS group. However, a PWS-specific general movements pattern was not observed. We highlight that pursuing in-depth knowledge within and beyond the motor domain in different groups has the potential to better understand different conditions, improve accurate diagnosis and individualised therapy, and contribute to deep phenotyping for precision medicine.

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Section: Discussionmentioning

confidence: 99%

Building Blocks for Deep Phenotyping in Infancy: A Use Case Comparing Spontaneous Neuromotor Functions in Prader-Willi Syndrome and Cerebral Palsy

Zhang

Wang

Shen

et al. 2023

JCM

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A Roadmap for Technological Innovation in Multimodal Communication Research

Gregori,

Amici,

Brilmayer

et al. 2023

Lecture Notes in Computer Science

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Multimodal communication research focuses on how different means of signalling coordinate to communicate effectively. This line of research is traditionally influenced by fields such as cognitive and neuroscience, human-computer interaction, and linguistics. With new technologies becoming available in fields such as natural language processing and computer vision, the field can increasingly avail itself of new ways of analyzing and understanding multimodal communication. As a result, there is a general hope that multimodal research may be at the "precipice of greatness" due to technological advances in computer science and Supported by the DFG priority program Visual Communication (ViCom).

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Infant Movement Detection via Eigenvalue-Entropy Based Subspace Method

Camelo,

Gatto,

Mendonça

et al. 2024

Anais Do XXIV Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2024)

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The early identification of anomalous movements in infants is crucial for intervening in potential neuromotor development disorders. The clinical method General Movement Assessment (GMA) is devoted to this identification task. However, since GMA is intensive and requires experts, new machine learning-based approaches and keypoints extracted from videos have emerged. However, challenges such as the underrepresentation of infants with writhing movements (WM)—general movements presented by infants in their first weeks of life; the scarcity of public datasets; and the fact that only video segments showing infants performing movements must be analyzed, are limitations to identify anomalous movements in infants automatically. This work introduces a method which uses spatial distance features extracted from skeletal data and employs subspace method based on the statistical analysis of the eigenvalue-entropy to enhance the detection of infants movements in video data, especially video from infants exhibiting WMs. The proposed method applies a subspace approach as an initial step to filter infant movements for further detection and subsequent classification, aiming to improve the detection and understanding of these critical early indicators. The results show that the proposed method is able to detect subtle nuances in infant movements more effectively than the baseline method, making it a promising tool for automatic developmental monitoring.

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Open Video Data Sharing in Developmental and Behavioural Science

Cited by 3 publications

References 0 publications

Building Blocks for Deep Phenotyping in Infancy: A Use Case Comparing Spontaneous Neuromotor Functions in Prader-Willi Syndrome and Cerebral Palsy

Building Blocks for Deep Phenotyping in Infancy: A Use Case Comparing Spontaneous Neuromotor Functions in Prader-Willi Syndrome and Cerebral Palsy

A Roadmap for Technological Innovation in Multimodal Communication Research

Infant Movement Detection via Eigenvalue-Entropy Based Subspace Method

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