Dimension-Wise Feature Selection of Deep Learning Models for In-Air Signature Time Series Analysis Based on Shapley Values

Yuheng Guo, Lingfeng Zhang, Yepeng Ding, Junwei Yu, Hiroyuki Sato

Abstract: This paper performs a comprehensive evaluation of Smartwatch in-air signature classification based on multiple deep learning models. We leverage the Shapley value in dimension-wise feature selection to provide the in-air signature community with the most and least dominant dimension regarding the accuracy of in-air signature classification. Our experiment results highlight InceptionTime as the top-performing model, achieving an accuracy of 97.73%. Through our Shapley Value analysis, among all the sensors embedded in the Smartwatch, we find that the y dimension of the gyroscope and the z dimension of the gyroscope contribute the most to classification accuracy with 12.57% and 12.51% respectively, while the x dimension of the accelerometer produces the least contribution with 8.71%.