UCSF researchers introduced adaptive deep brain stimulation (DBS) that changes in real time based on a person’s walking behavior, aiming to improve Parkinson’s symptom control beyond what traditional fixed-parameter DBS delivers. The approach is designed to tailor stimulation dynamically to gait and step patterns. For DBS in Parkinson’s, the core limitation has been that stimulation settings often optimized for tremor or rigidity may not match task-specific motor needs. By linking DBS parameters to real-time locomotion signals, the work points to a more responsive therapy model for mobility impairments.