Fig. 4: Demonstration of an i-US integrated glove (i-Glove) for finger-air-writing application.

a Schematic illustration of a finger-air-writing application. A participant wearing the i-Glove writes some characters, e.g., U, O, M, 1, 8, 2, 4, in the free space using his index finger. The four unimodal sensors of the i-US collect the corresponding signal of each character. The acquired signals are input into a pre-trained convolutional neural network (CNN) program after fast fourier transform (FFT). Finally, the program classifies the characters based on the signals. The bottom first two photographs show the i-US positions in the i-Glove at the side and top views. The third photograph shows the inside view of the i-Glove, in which the i-US is inserted into a transparent pocket of the index finger. The last photograph shows four unimodal sensors of the i-US. b Index finger motion classification. Top photographs show index finger motions (bending, shearing, turning, and flexion and extension), and the bottom panels show corresponding raw output voltages from four unimodal sensor channels of the i-US. Blue line: tension sensor channel; green line: bend sensor channel; red line: twist sensor channel; cyan line: shear sensor channel. c Top panel shows writing characters, and the bottom panel shows corresponding raw output voltages of four unimodal sensor channels of the i-US. All channels are the same as those of b. d Confusion matrix for the thirteen characters’ classification accuracy and its mean accuracy (top) when using four unimodal sensor channels of the i-US.