ViT-BiLSTM Multimodal Learning for Paediatric ADHD Recognition: Integrating Wearable Sensor Data with Clinical Profiles.

ADHD classification has traditionally relied on accelerometer-derived tabular features, which summarise static activity but fail to capture spatial-temporal patterns, potentially limiting model performance. We developed a multimodal deep learning framework that transforms raw accelerometer signals into images and integrates them with clinical tabular data, enabling the joint exploration of dynamic activity patterns and static clinical characteristics. Data were collected from children aged 7-13 years, including accelerometer recordings from Apple Watches and clinical measures from standardised questionnaires. Deep learning models for image feature extraction and multiple fusion strategies were evaluated to identify the most effective representation and integration method. Our analyses indicated that combining activity images with clinical variables facilitated the classification of ADHD, with the ViT-BiLSTM model using cross-attention fusion achieving the highest performance. These findings suggest that multimodal learning can become a robust approach to ADHD classification by leveraging complementary information from activity dynamics and clinical data. Our framework and code will be made publicly available to support reproducibility and future research.