Open video data sharing in developmental and behavioural science

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

doi:10.48550/arxiv.2207.11020

Cited by 2 publications

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“…Although precise and wellestablished, such methods have the disadvantage of being bound to a lab than more recent contact-free approaches like video recordings for the analyses of facial expression, acoustic analysis for automatic speech recognition, and recognition of emotions in speech [40,170]. Video-based assessment of body pose is also increasingly used in clinical populations [111], however, developing efficient machine learning algorithms for semantic analysis of body pose and gestures remains a challenge. Ultimately, this depends on the exact definition of semantically interpretable body configurations for the respective situation (e.g., a gesture "vocabulary", categories of the emotional expression of movement).…”

Section: Interaction With Other Fieldsmentioning

confidence: 99%

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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Section: Interaction With Other Fieldsmentioning

confidence: 99%

A Roadmap for Technological Innovation in Multimodal Communication Research

Gregori,

Amici,

Brilmayer

et al. 2023

Lecture Notes in Computer Science

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“…There is also a significant use of high-complexity methods for classifying normal and abnormal movements, such as Graph Convolutional Networks (GCN) [Groos et al 2022] and Transformers [Gao et al 2023]. Moreover, there is a limited number of publicly available datasets for classifying and recognizing infant movements [Marschik et al 2022]. Another observed issue in the collected database is the presence of video segments where the infant shows no movement, which can lead to redundant analysis both for the classifier and for the GMA expert, by analysing data that does not contain relevant information.…”

Section: Introductionmentioning

confidence: 99%

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 2 publications

References 0 publications

A Roadmap for Technological Innovation in Multimodal Communication Research

A Roadmap for Technological Innovation in Multimodal Communication Research

Infant Movement Detection via Eigenvalue-Entropy Based Subspace Method

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