InterventionLens: A Multi-Agent Framework for Detecting ASD Intervention Strategies in Parent-Child Shared Reading
arXiv:2603.13710v1 Announce Type: new Abstract: Home-based interventions like parent-child shared reading provide a cost-effective approach for supporting children with autism spectrum disorder (ASD). However, analyzing caregiver intervention strategies in naturalistic home interactions typically relies on expert annotation, which is costly, time-intensive, and difficult to scale. To address this challenge, we propose InterventionLens, an end-to-end multi-agent system for automatically detecting and temporally segmenting caregiver intervention strategies from shared reading videos. Without task-specific model training or fine-tuning, InterventionLens uses a collaborative multi-agent architecture to integrate multimodal interaction content and perform fine-grained strategy analysis. Experiments on the ASD-HI dataset show that InterventionLens achieves an overall F1 score of 79.44\%, outperforming the baseline by 19.72\%. These results suggest that InterventionLens is a promising system
arXiv:2603.13710v1 Announce Type: new Abstract: Home-based interventions like parent-child shared reading provide a cost-effective approach for supporting children with autism spectrum disorder (ASD). However, analyzing caregiver intervention strategies in naturalistic home interactions typically relies on expert annotation, which is costly, time-intensive, and difficult to scale. To address this challenge, we propose InterventionLens, an end-to-end multi-agent system for automatically detecting and temporally segmenting caregiver intervention strategies from shared reading videos. Without task-specific model training or fine-tuning, InterventionLens uses a collaborative multi-agent architecture to integrate multimodal interaction content and perform fine-grained strategy analysis. Experiments on the ASD-HI dataset show that InterventionLens achieves an overall F1 score of 79.44\%, outperforming the baseline by 19.72\%. These results suggest that InterventionLens is a promising system for analyzing caregiver intervention strategies in home-based ASD shared reading settings. Additional resources will be released on the project page.
Executive Summary
The article introduces InterventionLens, a multi-agent framework designed to detect autism spectrum disorder (ASD) intervention strategies in parent-child shared reading settings. This system aims to automate the analysis of caregiver intervention strategies, reducing the need for expert annotation. InterventionLens achieves an overall F1 score of 79.44%, outperforming the baseline by 19.72%. The framework's effectiveness suggests its potential as a tool for supporting children with ASD in home-based settings.
Key Points
- ▸ InterventionLens is a multi-agent system for detecting ASD intervention strategies
- ▸ The framework integrates multimodal interaction content without task-specific model training
- ▸ Experiments on the ASD-HI dataset demonstrate InterventionLens's superior performance
Merits
Innovative Approach
InterventionLens offers a novel, automated solution for analyzing caregiver intervention strategies, addressing the scalability issues associated with expert annotation.
High Performance
The system's high F1 score indicates its effectiveness in detecting intervention strategies, making it a valuable tool for supporting children with ASD.
Demerits
Limited Generalizability
The framework's performance might be specific to the ASD-HI dataset and shared reading context, potentially limiting its applicability to other settings or populations.
Expert Commentary
The introduction of InterventionLens marks a significant step forward in the application of artificial intelligence to support the analysis of caregiver intervention strategies in ASD. By automating the detection of these strategies, InterventionLens has the potential to enhance the scalability and effectiveness of home-based interventions. However, further research is needed to fully explore the system's generalizability and to address potential ethical considerations related to the use of AI in healthcare settings. As the field continues to evolve, it will be essential to consider how technologies like InterventionLens can be integrated into existing healthcare frameworks to maximize their benefits.
Recommendations
- ✓ Conduct further studies to assess InterventionLens's performance across diverse populations and settings
- ✓ Explore the potential for integrating InterventionLens with other AI-powered tools to create comprehensive support systems for individuals with ASD