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Abstract <p>Idiopathic scoliosis (IS) is a complex three-dimensional spinal deformity involving lateral curvature and vertebral rotation, significantly impacting patients’ physical and psychological well-being. Current rehabilitation assessment for IS predominantly relies on static X-rays and clinical experience, which overlooks crucial dynamic information during movement and fails to quantify the spine’s functional capacity. To address this limitation, we developed a dynamic assessment system that integrates multimodal signals. The system combines surface electromyography (sEMG) and spatial positioning information (SPI) to monitor, guide, and evaluate the rehabilitation motions of IS patients. We validated the system’s effectiveness through experiments on both healthy subjects and IS patients. The results demonstrate its potential to enhance data-driven, unsupervised rehabilitation for IS patients and to support future clinical applications in personalized rehabilitation planning.</p> Graphic abstract <p></p>

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A novel multi-modal signals dynamic assessment method of idiopathic scoliosis patients for rehabilitation

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Mingjie Dong,
  • Chengyin Wang,
  • Yinbo Chen,
  • Yuechuan Zhang,
  • Zhuosong Bai,
  • Shuo Wang,
  • Jianguo Zhang,
  • Run Ji,
  • Jianfeng Li,
  • Bin Fang,
  • Qianyu Zhuang

摘要

Abstract

Idiopathic scoliosis (IS) is a complex three-dimensional spinal deformity involving lateral curvature and vertebral rotation, significantly impacting patients’ physical and psychological well-being. Current rehabilitation assessment for IS predominantly relies on static X-rays and clinical experience, which overlooks crucial dynamic information during movement and fails to quantify the spine’s functional capacity. To address this limitation, we developed a dynamic assessment system that integrates multimodal signals. The system combines surface electromyography (sEMG) and spatial positioning information (SPI) to monitor, guide, and evaluate the rehabilitation motions of IS patients. We validated the system’s effectiveness through experiments on both healthy subjects and IS patients. The results demonstrate its potential to enhance data-driven, unsupervised rehabilitation for IS patients and to support future clinical applications in personalized rehabilitation planning.

Graphic abstract