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

TL;DR

This study introduces a wearable sensor-based method using pelvic motion analysis to detect early signs of stroke risk, showing promising preliminary results for community screening.

Contribution

The paper presents a novel, low-cost wearable framework that quantifies gait variability and postural drift for early pre-stroke risk detection using machine learning.

Findings

Pelvic variability increases from control to stroke groups.

The classifier achieved an AUC of 0.785 for risk stratification.

The approach is feasible for non-invasive, scalable community screening.

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.