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The study reports that agitation—an early, subtle sign of distress—can be detected in autistic youth using multimodal wearable sensors. By fusing upper‑body movement, wrist‑worn physiological data, and vocalizations through pretrained foundation models, the authors achieved an AUC of 0.724 at the clinician‑annotated onset of agitation, with audio contributing the most signal. The approach works across 15 participants, showing that individualized agitation is detectable even before the onset and without needing a separate model per child.
arXiv:2608. 12548v1 Announce Type: cross Abstract: Dance imitation integrates motor planning, sensorimotor integration, and social cognition, offering a sensitive framework to characterize motor behavior in autism.
arXiv:2608. 09830v1 Announce Type: new Abstract: Body-focused repetitive behaviors, such as hair pulling and skin picking, are compulsive motor actions commonly associated with obsessive-compulsive and anxiety disorders.
The paper introduces Sensori, a self‑supervised foundation model that learns health representations from 24‑hour raw tri‑axial wrist movement data. Trained on 122,640 participants across the UK, China, and the US, Sensori captures diverse movement behaviours, demographics, health axes, and physical function. In independent cohorts, the model improved disease classification for 52 of 102 conditions and incident disease risk prediction for 26 of 87 conditions, especially for neurological and psychiatric disorders.
The study introduces a clinician‑in‑the‑loop benchmark to assess whether large language models can generate evidence‑grounded Brief Hierarchical Taxonomy of Psychopathology (B‑HiTOP) item profiles from multimodal data, including passive sensing, ecological momentary assessment, and questionnaires. Using the GLOBEM dataset, the authors create 14,592 participant‑day instances aligned to 29 B‑HiTOP items across five spectra, and evaluate evidence compatibility rather than diagnostic accuracy. Two‑stage prediction improves compatibility for EMA and questionnaire evidence but reduces it for passive sensing and combined evidence, yielding more conservative score distributions across models, spectra, and evidence settings.
arXiv:2411. 15240v5 Announce Type: replace-cross Abstract: Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflecting fine-grained temporal behavioral trends.