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Hand Gesture Segmentation Method using a Wrist-Worn Wearable Device
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 Title & Authors
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;
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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.
Gesture segmentation;Wearable computer;Adaptive threshold;Smart apparel;
 Cited by
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