Early Pre-Stroke Detection via Wearable IMU-Based Gait Variability and Postural Drift Analysis

Abstract

Early identification of individuals at risk of stroke remains a major clinical challenge, as prodromal motor im- pairments are often subtle and transient. In this pilot study, a wearable sensor-based framework is proposed for early pre- stroke risk screening using a single inertial measurement unit mounted on the sacral region to capture pelvic motion during gait and standing tasks. The pelvis is treated as a biomechanical proxy for global motor control, enabling the quantification of gait variability and postural drift as digital biomarkers of neurological instability. Raw inertial signals are processed using a sensor fusion pipeline to estimate pelvic kinematics, from which variability and nonlinear dynamic features are extracted. These features are subsequently used to train a machine learning model for risk stratification across control, pre-stroke, and stroke groups. Progressive increases in pelvic angular variability and postural instability are observed from the control to stroke groups, with the pre-stroke cohort exhibiting intermediate char- acteristics. As a proof-of-concept investigation, the proposed framework demonstrates the feasibility of using a minimal wearable configuration to capture pelvic micro-instability associ- ated with early cerebrovascular motor adaptation. The classifier achieves a macro-averaged area under the curve of 0.785, indicating preliminary discriminative capability between risk categories. While not intended for clinical diagnosis, the proposed approach provides a low-cost, non-invasive, and scalable solution for continuous community-level screening, supporting proactive intervention prior to the onset of major stroke events.