arXiv AI By Youxiang Sun, Yujie Nie, Zhuoru Wu, Mengyuan Yu, Wenkong Wang, Xiang Li, Yumei Wang, Xin Ma, Lizhou Fan

Task-evoked eye movements as candidate digital biomarkers of depression severity in adolescents and young adults

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv AI
Sep 4

GazeFS: Target-Centered Gaze-Trajectory Forecasting and Stabilization from Gaze-Head History

GazeFS is a model that predicts and stabilizes target‑centered gaze trajectories using a variable‑length gaze‑head history, without requiring target information during inference. It maps this history to the next target‑center direction and a short‑horizon Search/Focus estimate, improving focus target centering and reducing residual gaze error. Across 7,960 acquisition episodes from 30 participants, GazeFS reduces Focus episode bias, dispersion, and P90 target error by 0.182°, 0.257°, and 0.400°, respectively, while maintaining high phase‑balanced accuracy and AUPRC.

By Yaozheng Xia, Zaiping Zhu, Bo Pang, Minghao Xie, Hui Li, Shaorong Wang, Sheng Li
arXiv AI
Sep 7

BioSync: Transformer-Based Cross-Modal Fusion for a Multimodal Physiological Digital Biomarker

BioSync is a transformer-based model that fuses cardiac, neural, behavioral, and speech data from wearables and mobile devices into a continuous composite digital biomarker called the BioSync Index (BSI). The architecture uses multi-head self-attention on modality tokens and a linear branch for feature concatenation, inspired by latent-variable measurement theory. Evaluations on synthetic cohorts for cognitive decline and metabolic-autonomic conditions show BioSync achieving AUCs of 0.928 and 0.764 accuracy/F1 of 0.766, outperforming simple concatenation and other fusion strategies in most corruption scenarios.

By Seyed Mahmoud Sajjadi Mohammadabadi
arXiv AI
Sep 1

AOI-Net: Structural Face AOI-Guided Eye-Gaze Track Representation Learning for Autism Spectrum Disorder Detection

AOI-Net introduces a structural face AOI-guided Eye‑Gaze Track Network that jointly models short‑term temporal dynamics and AOI‑level structural organization for Autism Spectrum Disorder detection. The network uses a gating mechanism to adaptively combine complementary representations and incorporates class‑distribution‑aware learning to address the imbalance between ASD and typically developing participants. Experiments on a large clinical eye‑tracking database with over 1,300 participants demonstrate that AOI‑Net outperforms state‑of‑the‑art methods and offers interpretable gaze‑behavior modeling for scalable AI‑driven ASD screening.

By Zhanpei Huang, Binbin Sun, Jialiang Chen, Yiou Wang, Taochen Chen, Yuzhu Ji, Yiqun Zhang, Yiu-Ming Cheung
arXiv AI
Jul 29

DynaBridge: Dynamic Summary-Guided Cross-Task Multimodal Fusion for DASS-Structured Mental Health Assessment

arXiv:2607. 25679v1 Announce Type: cross Abstract: Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questionnaire labels.

By Shiyu Teng, Haichen Yu, Jiaqing Liu, Hao Sun, Yu Song, Shurong Chai, Ruibo Hou, Lanfen Lin, Yen-Wei Chen