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Journal of Graphics ›› 2026, Vol. 47 ›› Issue (3): 607-615.DOI: 10.11996/JG.j.2095-302X.2026030607

• Computer Graphics and Virtual Reality • Previous Articles     Next Articles

A multi-indicator fusion digital-twin evaluation method oriented to FMA-UE

WANG Kun1(), LI Yifan1, TIAN Hongliang2, ZHU Weiguang1, HUANG Yaning1   

  1. 1 Inner Mongolia University of Technology, Hohhot Inner Mongolia 010051, China
    2 Inner Mongolia Autonomous Region Civil Affairs Social Services Center, Hohhot Inner Mongolia 010091, China
  • Received:2025-10-29 Accepted:2026-03-10 Online:2026-06-30 Published:2026-06-30
  • Contact: WANG Kun
  • Supported by:
    Inner Mongolia Autonomous Region Key Research and Development and Achievement Transformation Project(2025YFHH0148)

Abstract:

To address the subjectivity and poor quantifiability of post-stroke upper-limb functional assessment, a digital-twin-based evaluation method targeted at the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE) was proposed and validated in a controlled study. PN3 sensors were used to capture pose data and a Dynamic Fusion Rating Algorithm (DFRA) was constructed to achieve item-level alignment with FMA-UE, outputting individual scores and identifying deficits. In a controlled comparative study with 30 participants, the system improved efficiency by approximately 40% relative to manual assessment. System scores were statistically equivalent to therapist scores within a ±3-point equivalence margin under the Two One-Sided tests (TOST) procedure, with overall errors of MAE=1.97 points and RMSE=2.14 points. Deficit identification achieved Top-1 agreement of 88.0% with therapists across the five FMA-UE domains. The proposed DFRA-plus-digital-twin approach enabled item-level automated scoring and deficit visualization while preserving clinical interpretability, markedly improving efficiency over traditional assessment and demonstrating practical deployability in engineering settings.

Key words: digital twin, Fugl-Meyer assessment for upper extremity, motor function, feature fusion, automated assessment

CLC Number: