An AI-based Framework For Parent-child Interaction Analysis
Creators: name: Nikbakhtbideh, Behnam
Publisher: name: Graduate Studies
Year: 2023
Subjects: subject: NLP
subject: Machine learning
subject: Deep learning
subject: PCITS
subject: Parent-child interaction teaching scale
subject: Parent-child interactions
subject: Artificial Intelligence

description: The quality of parent-child interactions is foundational to children's social-emotional and cognitive development, as well as their lifelong mental health. The Parent-Child Interaction Teaching Scale (PCITS) is a well-established and effective tool used to measure parent-child interaction quality. It is utilized in both public health settings and basic and applied research studies to identify problem areas within parent-child interactions. However, like other observational measures of parent-child interaction quality, the PCITS can be time-consuming to administer and score, which limits its wider implementation. Therefore, the main objective of this research is to organize a framework for the recognition of behavioural symptoms of the child and parent during interventions. Based on the literature on interactive parent-child behaviour analysis, we categorized PCITS labels into three modalities: language, audio, and video. Some labels have dyadic actors, while others have a single actor (either the parent or child). In addition, within each modality, there are technical issues, considerations, and limitations in terms of artificial intelligence. Hence, we divided the problem into three modalities, proposed models for each modality, and a solution to combine them. Firstly, we proposed a model for recognizing action-related labels (video). These labels are interactive and involve two actors: the parent and the child. We conducted a feature extraction algorithm to produce semantic features passed through a feature selection algorithm to extract the most meaningful semantic features from the video. We chose this method due to its lower data requirement compared to other modalities. Also, because of using 2D video files, the proposed feature extraction and selection algorithms are to handle the occlusion and natural conditions like camera movement, Secondly, we proposed a model for recognizing language- and audio-related labels. These labels represent a single-actor role for the parent, as children are not yet capable of producing meaningful text in the intervention videos. To develop this model, we conducted research on a similar dataset to utilize transfer learning between two problems. Therefore, the second part of this research is associated with working on this text dataset. Third, we focused on multi-modal aspects of the work. We conducted experiments to determine how to integrate the prior work into our model. We also provided an ensemble model, which combined the modalities of language and audio based on the semantic and syntactic characteristics of the text. This ensemble model provides a baseline for developing further models with different aspects and modalities. Finally, we provided a roadmap to support more labels that were not covered in this research due to not reaching enough samples. Our proposed framework includes a labelling system that we developed in the primary stages of the research to gather labelled data. This system also plays a role to be integrated with AI modules to provide auto-recognition of the behavioural labels in parent-child interaction videos to the nurses.