Mixed reality-based upper limb motor function assessment system: Feasibility and reliability study for PSCI rehabilitation

Abstract

Post-stroke cognitive impairment (PSCI) is frequently accompanied by upper limb motor dysfunction, while traditional assessment methods are limited by high cognitive demands and subjectivity. Mixed reality (MR) technology can balance immersion and comprehensibility, offering a novel approach for automated motor function evaluation.

In this study, we developed an MR-based upper limb assessment system that integrates a virtual demonstration hand with wearable sensors to capture kinematics during seven standardized tasks derived from the Fugl-Meyer Assessment (FMA) and Action Research Arm Test (ARAT). Ten healthy participants completed video-, VR-, and MR-based assessments, followed by a one-week retest of MR and VR conditions. Subjective experience was evaluated using multidimensional questionnaires, while the reliability of objective kinematic features was examined using intraclass correlation coefficients (ICC), standard error of measurement (SEM), and coefficient of variation (CV%).

Compared with video and VR, MR achieved significantly higher ratings in usefulness, comprehensibility, satisfaction, and willingness to continue, while inducing lower fatigue and cybersickness. Presence improved after MR exposure, whereas disorientation-related cybersickness decreased. Test–retest analysis indicated good reliability for path length (ICC ​= ​0.85), average velocity (ICC ​= ​0.76), mean acceleration (ICC ​= ​0.77), and forearm velocity peaks (ICC ​= ​0.77).

The MR system demonstrated good feasibility, acceptance, and kinematic quantification performance. By reducing cognitive burden and enabling objective assessment, it shows potential as a tool for rehabilitation monitoring in PSCI populations.