Gait-Based Computational Phenotyping of Psychomotor and Affective Disturbance in Psychiatry

Psychiatric disorders are often accompanied by observable disturbances in psychomotor function and affective expression. Such disturbances may manifest in gait through changes in pace, rhythm, stability, coordination, posture, and whole-body movement organization. Because gait reflects integrated motor, cognitive, and affective processes, it provides a promising behavioral window into clinically relevant alterations in psychiatric conditions. A key challenge in psychiatry is not merely to classify diagnostic categories, but to identify quantifiable symptom dimensions that cut across disorders and vary over time. Psychomotor slowing or agitation, affective dysregulation, fatigue, reduced motor expressivity, and behavioral instability are all highly relevant to psychiatric evaluation, yet they remain difficult to measure consistently in routine practice. This Research Topic therefore focuses on gait-based computational phenotyping: the use of gait-derived behavioral signatures to characterize symptom expression, severity, patient stratification, and longitudinal change in psychiatric populations.We welcome studies that use gait as the primary behavioral substrate for clinically grounded computational modeling in psychiatry. Topics of interest include gait- and skeleton-based digital biomarkers of psychiatric symptoms; computational characterization of psychomotor retardation, agitation, and affect-related movement patterns; subject-invariant and condition-robust gait representation learning; interpretable models linking gait features to symptom dimensions; and longitudinal or real-world gait assessment for monitoring clinical trajectories. Studies incorporating additional modalities may also be considered, provided that gait remains the central phenotyping anchor and that multimodal integration clearly enhances psychiatric interpretation.Particular interest will be given to work that demonstrates clear clinical relevance through validation against symptom scales, clinician-rated assessments, functional outcomes, or longitudinal changes. We also encourage contributions on self-supervised learning, domain adaptation, and privacy-aware or resource-efficient sensing when these approaches support robust and deployable gait-based assessment. By centering on gait as an objective and clinically interpretable behavioral marker, this Research Topic aims to advance computational phenotyping frameworks that are better aligned with translational psychiatry and the development of practical digital biomarkers for mental health. Topic editors: Haifeng Lu, Haocong Rao, Tao Wang, Ren Huixia, Xuntao Yin Source: https://www.frontiersin.org/research-topics/79309/gait-based-computational-phenotyping-of-psychomotor-and-affective-disturbance-in-psychiatry