Adaptive deep brain stimulation for gait using a device embedded inertial sensor
Recent studies show that pallidal and subthalamic local field potentials (LFPs) encode locomotor state and can guide adaptive deep brain stimulation (DBS) for gait impairment in Parkinson's disease. Here, in one participant implanted with the Picostim DyNeuMo-2c, we demonstrate a simpler and more direct approach for inferring locomotor state using the device's onboard accelerometer. Triaxial acceleration was classified independently on each axis to select among preconfigured stimulation programs. Using a cranially mounted digital twin, we characterized inertial signatures across medication and activity states, developed a classifier that distinguished walking from rest while rejecting tremor, and verified the intended stimulation switches during walking. In an exploratory comparison, a gait-adaptive program improved objective gait measures relative to open-loop stimulation optimised for resting tremor. These findings provide a first-in-human demonstration of the feasibility of device-embedded inertial sensing for gait-responsive DBS. They establish a practical framework for further evaluation in larger cohorts.