Abstract
A machine learning approach is investigated in this study to detect a finger tapping on a handheld sur-face, where the movement of the surface is observed visually; however, the tapping finger is not directly visible. A feature vector extracted from consecutive frames captured by a high-speed camera that observes a surface patch is input to a convolutional neural network to provide a prediction label indicating whether the surface is tapped within the sequence of consecutive frames (“tap”), the surface is still (“still”), or the surface is moved by hand (“move”). Receiver operat-ing characteristics analysis on a binary discrimination of “tap” from the other two labels shows that true positive rates exceeding 97% are achieved when the false positive rate is fixed at 3%, although the generaliza-tion performance against different tapped objects or different ways of tapping is not satisfactory. An in-formal test where a heuristic post-processing filter is introduced suggests that the use of temporal history information should be considered for further improve-ments. biho.
Original language | English |
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Pages (from-to) | 484-493 |
Number of pages | 10 |
Journal | Journal of Robotics and Mechatronics |
Volume | 33 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2021 |
Keywords
- Convolutional neural network
- Finger tapping
- High-speed vision
- Touch interface
ASJC Scopus subject areas
- Computer Science(all)
- Electrical and Electronic Engineering