Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada.
PLoS One. 2013 May 2;8(5):e63216. doi: 10.1371/journal.pone.0063216. Print 2013.
Grip kinetics and their variation are emerging as important considerations in the clinical assessment of handwriting pathologies, fine motor rehabilitation, biometrics, forensics and ergonomic pen design. This study evaluated the intra- and inter-participant variability of grip shape kinetics in adults during signature writing. Twenty (20) adult participants wrote on a digitizing tablet using an instrumented pen that measured the forces exerted on its barrel. Signature samples were collected over 10 days, 3 times a day, to capture temporal variations in grip shape kinetics. A kinetic topography (i.e., grip shape image) was derived per signature by time-averaging the measured force at each of 32 locations around the pen barrel. The normalized cross correlations (NCC) of grip shape images were calculated within- and between-participants. Several classification algorithms were implemented to gauge the error rate of participant discrimination based on grip shape kinetics. Four different grip shapes emerged and several participants made grip adjustments (change in grip shape or grip height) or rotated the pen during writing. Nonetheless, intra-participant variation in grip kinetics was generally much smaller than inter-participant force variations. Using the entire grip shape images as a 32-dimensional input feature vector, a K-nearest neighbor classifier achieved an error rate of 1.2±0.4% in discriminating among participants. These results indicate that writers had unique grip shape kinetics that were repeatable over time but distinct from those of other participants. The topographic analysis of grip kinetics may inform the development of personalized interventions or customizable grips in clinical and industrial applications, respectively.
握力动力学及其变化在手写病理学的临床评估、精细运动康复、生物识别、法医学和人体工程学笔设计中越来越受到重视。本研究评估了成年人签名书写过程中握力形状动力学的个体内和个体间变异性。20 名成年参与者使用测量其笔杆上所受力量的仪器化笔在数字化写字板上书写。签名样本在 10 天内每天采集 3 次,以捕捉握力形状动力学的时间变化。通过在笔杆周围的 32 个位置上测量力的时间平均值,为每个签名得出一个动力学地形(即握力形状图像)。在参与者内和参与者间计算握力形状图像的归一化互相关系数(NCC)。实现了几种分类算法来衡量基于握力动力学的参与者识别的错误率。出现了四种不同的握力形状,一些参与者在书写过程中进行了握力调整(握力形状或握力高度的变化)或旋转了笔。尽管如此,握力动力学的个体内变化通常比个体间力变化小得多。使用整个握力形状图像作为 32 维输入特征向量,K-最近邻分类器在区分参与者方面的错误率为 1.2%±0.4%。这些结果表明,书写者具有独特的握力形状动力学,随着时间的推移具有可重复性,但与其他参与者的动力学不同。握力动力学的地形分析可能分别为临床和工业应用中的个性化干预或可定制握把的开发提供信息。