Prediction for prospective falls via gait evaluation using mobile devices for stroke survivors : a markerless motion analysis study

Abstract

Objective: Stroke survivors often experience hemiparetic lower extremity impairment, which increases fall risk. This study investigates prospective fall risk prediction using gait kinematic markers analyzed through a markerless motion capture system on mobile devices for participants with chronic stroke. Design: A prospective cohort study. Setting: Laboratory setting, with three iPad Pros positioned at the start, end, and lateral points along a 3-meter walkway. Participants: Adults with hemiplegic stroke (Modified Functional Ambulation Classification ≥ III) and age-matched healthy controls, all without a recent fall. Main measures: Gait parameters including stride length, cadence, step width, stance/swing time, double support time at baseline, and fall history interview over the 18-month period following the walking experiment. Results: Fifty healthy adults and 46 participants with chronic stroke were recruited. The 18-month prevalence for fallers in participants with stroke was 13%. Participants with stroke demonstrated a slower walking speed, a shorter step width, and a longer standing time than the healthy adults. Cadence, stride length, stance time, and swing time were strong predictors of fallers among participants with chronic stroke. The relative risks for low cadence, low swing phase, and high stance phase were 2.163, 2.002, and 2.142, respectively. Conclusion: Our findings support the importance of using gait parameters obtained from the markerless motion capture system on mobile devices to predict prospective fall risk in the stroke population. Future research with larger, diverse cohorts of the stroke population using markerless motion capture is recommended to validate and refine the fall prediction models.