A wireless dynamic gesture user interface for HCI using hand data glove

Prasad, Shitala; Kumar, Piyush; Sinha, Kumari Priyanka

doi:10.1109/ic3.2014.6897148

Cited by 11 publications

(3 citation statements)

References 15 publications

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“…Shitala captured a gesture with a data glove and mapped it to a keyboard to control a media player [11]. The gesture was recognized using the data glove and a decision tree.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Recognition Method of Counting Fingers for Natural User Interface

2016

KSII TIIS

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Communication occurs through verbal elements, which usually involve language, as well as non-verbal elements such as facial expressions, eye contact, and gestures. In particular, among these non-verbal elements, gestures are symbolic representations of physical, vocal, and emotional behaviors. This means that gestures can be signals toward a target or expressions of internal psychological processes, rather than simply movements of the body or hands. Moreover, gestures with such properties have been the focus of much research for a new interface in the NUI/NUX field. In this paper, we propose a method for recognizing the number of fingers and detecting the hand region based on the depth information and geometric features of the hand for application to an NUI/NUX. The hand region is detected by using depth information provided by the Kinect system, and the number of fingers is identified by comparing the distance between the contour and the center of the hand region. The contour is detected using the Suzuki85 algorithm, and the number of fingers is calculated by detecting the finger tips in a location at the maximum distance to compare the distances between three consecutive dots in the contour and the center point of the hand. The average recognition rate for the number of fingers is 98.6%, and the execution time is 0.065 ms for the algorithm used in the proposed method. Although this method is fast and its complexity is low, it shows a higher recognition rate and faster recognition speed than other methods. As an application example of the proposed method, this paper explains a Secret Door that recognizes a password by recognizing the number of fingers held up by a user.

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“…Shitala captured a gesture with a data glove and mapped it to a keyboard to control a media player [11]. The gesture was recognized using the data glove and a decision tree.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Recognition Method of Counting Fingers for Natural User Interface

2016

KSII TIIS

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“…The traditional mode of human-computer interaction-from the original keyboard to the modern mouse, joystick, and wireless input device-greatly facilitates the interaction between people and computers and makes it easier for people to operate computers and increase work efficiency. 1 However, this mode of interaction cannot completely meet the demands of human-computer interaction due to its dependence on additional input hardware devices. To solve this problem, hands and arms are utilized in combination.…”

Section: Introductionmentioning

confidence: 99%

Wearable sensing devices for upper limbs: A systematic review

Dong

Fang

et al. 2020

Proc Inst Mech Eng H

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Wearable sensing devices, which are smart electronic devices that can be worn on the body as implants or accessories, have attracted much research interest in recent years. They are rapidly advancing in terms of technology, functionality, size, and real-time applications along with the fast development of manufacturing technologies and sensor technologies. By covering some of the most important technologies and algorithms of wearable devices, this paper is intended to provide an overview of upper-limb wearable device research and to explore future research trends. The review of the state-of-the-art of upper-limb wearable technologies involving wearable design, sensor technologies, wearable computing algorithms and wearable applications is presented along with a summary of their advantages and disadvantages. Toward the end of this paper, we highlight areas of future research potential. It is our goal that this review will guide future researchers to develop better wearable sensing devices for upper limbs.

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“…From these works, it has been identified the following specified shortcomings [3,4,5,6,7,8,9,10,11,12,13,14,15]:…”

Section: Related Workmentioning

confidence: 99%