Figure 5. Results of the gesture-recognition experiment: a) Gestures with corresponding time-domain signals measured by the three nanofiber-based pressure-sensor units (NFPSUs). b) Hands of four testers with different physiques. c-f) Classifiers obtained from different testers.
To characterize the adaptability and gesture-recognition accuracy of the GRW, four testers with different physiques were employed (Figure 5b) . Each tester performed each gesture ten times to update the corresponding databases. The corresponding classifiers were obtained by training new SVM classification models with the updated databases and corresponding gesture labels. Figure 5c shows the classifier obtained from Tester 1. Twelve gestures were successfully recognized with an accuracy of 93.2%, which was comparable to or slightly higher than that reported for GRWs with more than five electrical sensors.[14-16] The classifiers obtained from the other testers are shown in Figure 5d-f . The slight fluctuations in the recognition accuracy may be attributed to different physiques. Specifically, the subcutaneous fat of the chubby tester (Tester 2) reduced the degree of finger movement-related deformation, which slightly decreased the recognition accuracy. Nevertheless, the excellent adaptability of the proposed GRW can be seen in the high recognition accuracy (92%-94%) for all the testers, regardless of physique.