基于层析触觉传感器的手指运动分析系统,用于识别抓握手指的数量,以评估精细运动技能

Ryunosuke Asahi, Shunsuke Yoshimoto, Hiroki Sato
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引用次数: 0

摘要

手指运动与自闭症谱系障碍(ASD)等发育障碍密切相关。这些结果表明,手指运动的定量评估可以应用于婴幼儿认知能力下降和自闭症的早期诊断。因此,我们开发了一种基于层析触觉传感器的手指运动自动分析系统,该系统采用具有连续传感表面的耦合导体和具有高自由度形状的导电材料。此外,我们开发了两个圆柱形传感器,直径分别为25和50毫米,利用几何形状的高自由度。为了评估开发的传感器,我们进行了两项验证-60段交叉验证和holdout验证-以确定成年人参与抓取的手指数量。使用AlexNet对使用的重建图像进行识别;采用k近邻(KNN)和支持向量机(SVM)对电压矢量进行测量。结果表明,两种验证的所有参与者均获得了超过机会水平的准确度。此外,使用测量的电压矢量获得了约90%的平均精度。总之,研究结果表明,该装置可用于婴儿ASD的早期诊断和老年人认知能力下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tomographic tactile sensor-based finger motion analysis system to identify number of grasping fingers for evaluating fine motor skills
Finger movements are closely related to development disabilities such as autism spectrum disorder (ASD). These results suggest that a quantitative evaluation of finger movements may be applied to the early diagnosis of cognitive decline and autism in infants and young children. Therefore, we developed a novel automatic finger motion analysis system based on a tomographic tactile sensor by-using coupled conductors with a continuous sensing surface and conductive material with a high degree of freedom (DOF) of shape. Further, we developed two cylindrical sensors, with diameters of 25 and 50 mm, utilizing the high DOF of geometry. To evaluate the developed sensors, we conducted two validations-60-segment cross-validation and holdout validation–to identify the number of fingers engaged in grasping by adults. AlexNet was used for identification of the used reconstruction image; k-nearest neighbors (KNN) and Support vector machine (SVM) were used for the measurement of the voltage vector. According to the results, these accuracies exceeding the chance level was obtained for all participants of both validations. In addition, an average accuracy of approximately 90% was acquired using the measured voltage vector. In conclusion, the study findings indicate that the proposed device could be used for early diagnosis of ASD in infants and cognitive decline in older adults.
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