An Assessment System for Post-Stroke Manual Dexterity Using Principal Component Analysis and Logistic Regression
An Assessment System for Post-Stroke Manual Dexterity Using Principal Component Analysis and Logistic Regression
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DOI：
10.1109/tnsre.2019.2928719
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发表时间：
2019-08-01
影响因子：
4.9
通讯作者：
Hwang, Yi-Ting
中科院分区：
文献类型：
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作者：
Lin, Bor-Shing;Lee, I-Jung;Hwang, Yi-Ting
手功能评估对于中风患者至关重要，他们必须在康复期间进行定期重复性任务。然而，传统的评估方法是主观的，并不统一的医生。本文提出了一种新的方法来分析从一个数据手套配备了16个六轴惯性测量单元的原始数据。该方法可以为医生提供准确的辅助，客观地评估患者的手功能。三个任务（拇指任务，抓地力的任务，和卡片转动任务）进行评估参与者的手功能。通过主成分分析提取每个任务和总体评价中手功能的代表性参数，并用于建立Logistic回归模型。结果显示，所有三个任务都可以用来完美地预测健康受试者和中风受试者，拇指任务表现出手部功能障碍严重程度的最高预测准确性。总体而言，所提出的方法可以作为一个有效的方法，为医生评估中风患者的手功能。
Hand function assessment is crucial for patients with stroke, who must perform regular repetitive tasks during rehabilitation. However, the conventional evaluation method is subjective and not uniform among physicians. A novel method is proposed in this paper to analyze raw data from a data glove equipped with 16 six-axis inertial measurement units. The proposed method can provide accurate assistance to physicians and objectively assess patients' hand function. Three tasks (the thumb task, the grip task, and the card-turning task) were conducted to evaluate participants' hand function. Representative parameters of hand function in each task and overall evaluation were extracted through principal component analysis and used to develop logistic regression models. The results revealed that all three tasks can be used to perfectly predict healthy subjects and subjects with stroke, with the thumb task exhibiting the highest predictive accuracy for the severity of hand dysfunction. Overall, the proposed method can serve as an efficient method for physicians to assess the hand function of patients with stroke.