Hand Gesture Segmentation Method using a Wrist-Worn Wearable Device

Dong-Woo Lee; Yong-Ki Son; Bae-Sun Kim; Minkyu Kim; Hyun-Tae Jeong; Il-Yeon Cho

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Dong-Woo Lee (ETRI (Electronics and Telecommunications Research Institute))
Yong-Ki Son (ETRI (Electronics and Telecommunications Research Institute))
Bae-Sun Kim (ETRI (Electronics and Telecommunications Research Institute))
Minkyu Kim (ETRI (Electronics and Telecommunications Research Institute))
Hyun-Tae Jeong (ETRI (Electronics and Telecommunications Research Institute))

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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: This can be adopted to user interface for fashion apparel such as a smart watch and wrist band.

#Gesture segmentation #Wearable computer #Adaptive threshold #Smart apparel