Hand Gesture Segmentation Method using a Wrist-Worn Wearable Device Lee, Dong-Woo; Son, Yong-Ki; Kim, Bae-Sun; Kim, Minkyu; Jeong, Hyun-Tae; Cho, Il-Yeon;
Objective: We introduce a hand gesture segmentation method using a wrist-worn wearable device which can recognize simple gestures of clenching and unclenching ones' fist. Background: There are many types of smart watches and fitness bands in the markets. And most of them already adopt a gesture interaction to provide ease of use. However, there are many cases in which the malfunction is difficult to distinguish between the user's gesture commands and user's daily life motion. It is needed to develop a simple and clear gesture segmentation method to improve the gesture interaction performance. Method: At first, we defined the gestures of making a fist (start of gesture command) and opening one's fist (end of gesture command) as segmentation gestures to distinguish a gesture. The gestures of clenching and unclenching one's fist are simple and intuitive. And we also designed a single gesture consisting of a set of making a fist, a command gesture, and opening one's fist in order. To detect segmentation gestures at the bottom of the wrist, we used a wrist strap on which an array of infrared sensors (emitters and receivers) were mounted. When a user takes gestures of making a fist and opening one's a fist, this changes the shape of the bottom of the wrist, and simultaneously changes the reflected amount of the infrared light detected by the receiver sensor. Results: An experiment was conducted in order to evaluate gesture segmentation performance. 12 participants took part in the experiment: 10 males, and 2 females with an average age of 38. The recognition rates of the segmentation gestures, clenching and unclenching one's fist, are 99.58% and 100%, respectively. Conclusion: Through the experiment, we have evaluated gesture segmentation performance and its usability. The experimental results show a potential for our suggested segmentation method in the future. Application: The results of this study can be used to develop guidelines to prevent injury in auto workers at mission assembly plants.
Alon, J., Athitsos, V., Yuan, Q. and Sclaroff, S., A unified framework for gesture recognition and spatiotemporal gesture segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9), 1685-1699, 2009.
Junker, H., Amft, O., Lukowicz, P. and Troster, G., Gesture spotting with body-worn inertial sensors to detect user activities, Pattern Recognition, 41(6), 2010-2024, 2008.
Kahol, K., Tripathi, P., Panchanathan, S. and Rikakis, T., Gesture segmentation in complex motion sequences, Proceedings of the International Conference on Image Processing, 105-108, 2003.
Kulkarni, V.S. and Lokhande, S.D., Appearance based recognition of american sign language using gesture segmentation, International Journal on Computer Science and Engineering, 2(3), 560-565, 2010.
Otsu, N., A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66, 1979.