Multimodal AI-based Detection and Tracking of Autistic Behavioral Patterns

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Multimodal AI-based Detection and Tracking of Autistic Behavioral Patterns

i. Problem Definition

Autism Spectrum Disorder (ASD) presents a significant healthcare challenge due to its complex neurodevelopmental nature, characterized by deficits in social communication, repetitive behaviors, and sensory processing differences. Current diagnostic methods face two critical limitations:

They rely heavily on expert evaluation, which introduces subjective variability and accessibility barriers due to the limited availability of specialized healthcare providers.
Traditional clinical observations require time-intensive behavioral assessments, often leading to delayed intervention.

While existing AI-based detection models show promise, they typically focus on isolated symptoms, limiting their effectiveness in real-world clinical applications.

ii. Proposed Solution

To address these challenges, we propose a comprehensive system designed to recognize the presence and severity of multiple ASD symptoms, assisting doctors in the diagnostic process while accelerating assessments. By enabling a more efficient and objective evaluation, this system facilitates early diagnosis, leading to timely interventions that can significantly improve communication and social skills of autistic children. Additionally, the system can serve as a preliminary assessment tool for parents, allowing them to identify potential ASD signs in their children before seeking professional evaluation. This parallel processing approach, anchored by a detailed behavioral classification framework, enhances both the accuracy and efficiency of ASD detection, ensuring a more accessible and data-driven diagnostic process.

iii. Implementation Strategy

A multimodal AI-driven system operates through three modules working in concert to provide comprehensive ASD assessment.

1. Stereotypical Behavior Analysis

This model explores the analysis and monitoring of stereotypical behaviors in the SSBD and ESBD datasets using three independent deep learning approaches:

Temporal Convolutional Networks (TCN) and Multi-Stage TCN (MS-TCN) are utilized to model temporal dependencies. These models capture long-range temporal patterns, enabling robust sequential pattern recognition essential for understanding behavioral dynamics over time.
Spatio-Temporal Graph Convolutional Networks (ST-GCN) and Multi-Stage ST-GCN (MSTGCN) are employed to analyze the skeletal structure of pose data. These models capture both spatial relationships between joints and their temporal evolution across frames.
Multi-scale Vision Transformer (MViT-v2) is utilized to extract and analyze visual features from video data. By leveraging its hierarchical attention mechanisms, MViT-v2 captures both global and local visual representations. Additionally, MediaPipe is used for pose estimation, while Detectron2 ensures accurate child detection and localization.

Figure 1: A pipeline for analyzing stereotypical child behaviors. Combines Detectron2 for spatial child detection, MediaPipe for pose estimation, and a multi-scale vision transformer.

2. Audio-visual Data Analysis

The second module processes audio-visual data using the LLaVA architecture to analyze social interactions and communication patterns through the AV-ASD dataset, which encompasses 10 distinct behavioral categories:

Absence or avoidance of eye contact
Aggressive behavior
Hyper- or hypo-reactivity to sensory input
Non-responsiveness to verbal interaction
Non-typical language
Self-hitting or self-injurious behavior
Object lining-up
Self-spinning or spinning objects
Upper limb stereotypies
Background category

This comprehensive categorization ensures thorough coverage of key diagnostic indicators.

Figure 2: LLaVA-ASD: Instruction Tuning for LLaVA. Given a video preview and a text prompt, augmented with an audio caption and speech transcription. The output consists of multiple autism behavior labels presented in text format.

3. Creative Works

This project evaluates fine motor skills through drawing, coloring, and writing tasks using specialized convolutional neural networks, achieving over 95% accuracy in initial testing. Furthermore, an ongoing collaboration with Qasr El-Aini Hospital aims to collect a dataset representative of the Egyptian population, enhancing the model's applicability across diverse demographics.

Figure 3: Complete pipeline of the comprehensive system to analyze and monitor autism spectrum disorder symptoms.

iv. References

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