Objective Evaluation of Risk of Falls in Individuals with Chronic Stroke: Feasibility Study

Abstract

<h3>Research Objectives</h3> To evaluate the feasibility of the robotic fall-risk assessment performed on hunova to objectively quantify the risk of falls in individuals with chronic stroke. <h3>Design</h3> Cross-sectional study. <h3>Setting</h3> Balance Assessment and Training Lab. <h3>Participants</h3> Four individuals with hemiparesis post stroke, aged 62±2.7 years. <h3>Interventions</h3> Participants performed the fall-risk assessment on hunova- a commercial robotic platform for assessing balance. The assessment included multifaceted fall-driving components, including static and dynamic balance, sit-to-stand, limits of stability, responses to perturbations, walking speed and history of previous falls. A composite score for risk of falls, called as silver index (SI), between 0 (no risk) and 100 (high risk) was obtained using built-in machine learning based predictive model <h3>Main Outcome Measures</h3> silver index (risk of falls), Berg Balance Scale (BBS), Timed-Up and Go, Center of Pressure (CoP), walking speed <h3>Results</h3> The mean SI score for fall-risk for the stroke group was found to be 79.3±11.7 (min: 66, max: 91), suggesting moderate to high risk of falls. SI scores did not show any correlation with the functional outcomes of balance and mobility (BBS, TUG, walking speed). However, SI showed a linear trend with biomechanical outcomes (CoP displacement, CoP velocity, sway area, velocity during static and perturbed conditions) recorded during the SI assessments. <h3>Conclusions</h3> The current work shows the feasibility of using a robotic platform-based assessment to objectively quantify the risk of falls in individuals with chronic stroke. A large sample is needed to further prove the validity of the SI outcome before it is used for meaningful interpretations of the risk of falls. <h3>Author(s) Disclosures</h3> None.