Ankle-worn accelerometers for gait analysis in people with Alzheimer's disease and mild cognitive impairment:...

Clinical gait analysis is essential for understanding motor and cognitive contributions to mobility impairment. Continued methodological advancement in gait analysis is needed to detect important and subtle changes in walking behaviour that will inform this understanding. This study evaluated the reliability of a finite state machine (FSM) algorithm for stride segmentation during preferred and dual-task walking (DTW) and examined changes to temporal and accelerometry kinematic outcomes between tasks. Participants diagnosed with Alzheimer's disease or mild cognitive impairment completed a gait assessment as part of the Ontario Neurodegenerative Disease Research Initiative foundational study. Ankle-worn accelerometers and a GAITRite walkway captured data during preferred walking and three DTW conditions (counting backwards by ones, animal naming, counting backwards by sevens). Acceleration data were processed using the FSM algorithm to extract temporal and accelerometry kinematic outcomes defined by the FSM intra-stride segments. Stride time demonstrated excellent reliability (ICC ⩾ 0.97). Gait speed was significantly associated with gait variability during animal naming and counting backwards by sevens (p< 0.05), but not during counting backwards by ones. The FSM approach showed changes to intra-stride phases and accelerometry derived kinematics not seen with conventional stride-based approaches. There was a reduction in flat-foot phase and an increase in push-off phase across all dual-task conditions compared with PREF (p< 0.05), where conventional stance-phase segmentation did not show consistent differences. Kinematic outcomes were also significantly lower during DTW conditions when compared to preferred walking trials (p< 0.05) for the mid-swing peak amplitude and the slope of the accelerometer push-off. Findings demonstrate differences in performance between preferred and DTW conditions, the influence of gait speed on gait variability, and unique accelerometry-derived kinematics. The FSM segmentation method advances gait assessment by providing precise stride characteristics using low-cost, clinically accessible tools.