A gamified virtual reality and inertial measurement unit‐based framework for fine‐grained upper limb motor assessment in stroke patients

Stroke often leads to upper limb motor impairments, underscoring the need for precise assessment to guide personalized rehabilitation. Conventional clinical scales are limited by subjectivity and the absence of detailed kinematic analysis. To address this, we propose a novel assessment framework that integrates gamified virtual reality tasks with inertial measurement unit (IMU)–based kinematic analysis, enabling fine‐grained and autonomous evaluation of upper limb movements in stroke patients. Specifically, we introduce a region‐based motion normalcy index (rMNI) to quantify motor deficits across five spatial regions, offering a more nuanced characterization of movement impairments. Regression models, including elastic net, ridge, and least absolute shrinkage and selection operator regression, were trained on regional rMNI features to predict Fugl–Meyer assessment upper extremity (FMA‐UE) scores. Experiments with 12 stroke patients and 8 healthy controls demonstrated strong correlations between rMNI and both FMA‐UE total and subscale scores (| r | > 0.70), highlighting the ability of rMNI to spatially resolve motor dysfunction and identify impaired limbs. The best‐performing regression model achieved an R 2 of 0.90 and a Pearson's correlation coefficient of 0.95, indicating excellent predictive validity. These results suggest that the proposed framework is a promising tool for personalized rehabilitation, providing both fine‐grained spatial assessment and patient‐specific insights.