Assessment of change in drug-induced parkinsonism in daily-life gait using vision-based machine learning

Abstract

Drug-induced parkinsonism is a potentially reversible movement disorder that may be subtle and difficult to monitor in residential dementia care. Vision-based machine learning analysis of gait may provide unobtrusive longitudinal monitoring around medication changes. We conducted a two-center cohort study of 61 residents with dementia receiving psychotropic medication. Hallway gait sequences were recorded using ceiling-mounted cameras and converted to three-dimensional body pose. A TabPFN classifier estimated UPDRS-III item 29 gait severity for each walk. We evaluated associations between acute antipsychotic (AP) and antidepressant (AD) dose reduction/cessation ( ≥ 50% dose reduction or cessation, 0–21 days after change) and daily mean predicted gait severity using linear mixed-effects models in ever-exposed participants, with complementary equal-weight paired analyses among participants observed in both medication states. In mixed-effects models, acute AP dose reduction/cessation was associated with lower predicted gait severity ( β = − 0.127 , p = 0.022), whereas AD dose reduction/cessation was not ( β = − 0.060 , p = 0.210). Equal-weight paired analyses found no statistically significant participant-average within-person change among paired participants (AP mean change = 0.031, 95% CI [ − 0.114 , 0.176], p = 0.653; AD mean change = − 0.021 , 95% CI [ − 0.098 , 0.056], p = 0.575), consistent with heterogeneous individual responses. Vision-based monitoring captured within-participant gait variation around psychotropic dose-reduction periods, but findings are associative. Larger prospective studies with documented reasons for medication changes are needed before using such monitoring to guide medication decisions.

The authors' abstract, as published at the source. PLOS Digital Health, 2026 · DOI ↗