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.