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Purpose <p>This study aimed to develop and validate a computer vision-driven Digitalized Nine Hole Peg Test (D-NHPT) to assess hand function in stroke patients, examining the reliability and validity of extracted hand features and their ability to distinguish stroke patients from healthy subjects.</p> Methods <p>A customized data collection system and an improved test device using LMC2 captured hand-motion data. The study recruited 10 stroke patients and 5 healthy subjects. Statistical analyses included intraclass correlation coefficients (ICC) for reliability, p-values for discriminant validity (Mann-Whitney U test), and |r-scores| for convergent validity.</p> Results <p>The D-NHPT demonstrated high reliability (patient group ICC = 0.818–0.946; healthy group ICC = 0.785–0.904), significant discriminant validity (<i>p</i> &lt; 0.019), and strong convergent validity (|r-score|=0.671–0.909). Key features included motion speed, coordination, and task completion metrics, which effectively distinguished stroke patients from healthy subjects.</p> Conclusion <p>The D-NHPT provides a reliable, valid, and multidimensional assessment of hand function in stroke patients. Specific hand features are sensitive metrics for clinical evaluation, advancing digitalization of rehabilitation scales, and supporting personalized rehabilitation strategies.</p>

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Computer Vision-Driven Digitalization of the Nine Hole Peg Test Assessment Method: A Pilot Study

  • 期刊论文

  • 发表日期：

    2025年9月20日

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  • Yuxin Fan,
  • Aiqin Liu,
  • Qiurong Xie,
  • Qi Zhang,
  • Jianyu Zhao,
  • Sheng Quan Xie,
  • Bo Sheng

摘要

Purpose

This study aimed to develop and validate a computer vision-driven Digitalized Nine Hole Peg Test (D-NHPT) to assess hand function in stroke patients, examining the reliability and validity of extracted hand features and their ability to distinguish stroke patients from healthy subjects.

Methods

A customized data collection system and an improved test device using LMC2 captured hand-motion data. The study recruited 10 stroke patients and 5 healthy subjects. Statistical analyses included intraclass correlation coefficients (ICC) for reliability, p-values for discriminant validity (Mann-Whitney U test), and |r-scores| for convergent validity.

Results

The D-NHPT demonstrated high reliability (patient group ICC = 0.818–0.946; healthy group ICC = 0.785–0.904), significant discriminant validity (p < 0.019), and strong convergent validity (|r-score|=0.671–0.909). Key features included motion speed, coordination, and task completion metrics, which effectively distinguished stroke patients from healthy subjects.

Conclusion

The D-NHPT provides a reliable, valid, and multidimensional assessment of hand function in stroke patients. Specific hand features are sensitive metrics for clinical evaluation, advancing digitalization of rehabilitation scales, and supporting personalized rehabilitation strategies.