Flexible Gesture Recognition for Immersive Virtual Environments

Deller, Matthias; Ebert, Achim; Bender, Michael; Hagen, Hans

doi:10.1109/iv.2006.55

Cited by 15 publications

(11 citation statements)

References 6 publications

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“…Song et al [12] have proposed a multi-modal interface to control a 3D computer aided design (CAD) models using the finger movement and eye gaze motion. There are many other methods and techniques that use"s hand data glove for more efficient and accurate interaction and interface [16][17][18][19][20] but due to space limitation of this paper they are not mentioned in detail here. In 2013, again marker based mobile augmented reality system was proposed in traditional education system to modern it with latest technologies.…”

Section: Realitymentioning

confidence: 99%

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

Prasad

Kumar

Sinha

2014

2014 Seventh International Conference on Contemporary Computing (IC3)

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In this paper, DG5 hand data glove is used to design an intelligent and efficient human-computer interface to interact with VLC media player. It maps the static keyboard with dynamic human hand gestures with 22 Degree of Freedom (DoF) to interact more natural way with computer. The result is very much appreciated showing the confusion matrix of various gestures used. In this paper, 10 complex gestures are used, that is Play, Pause, Forward, Backward, Next, Previous, Stop, Mute, Full Screen, and Null gestures. To study about the human-hand gestures four different age groups are taken, User A (20-30 years), User B (31-45 years), User C (46-60 years), and User D (61-above years). The decision tree a powerful learning algorithm is used to classify these gestures correctly. This enhances the user's interaction level with immersion feeling in augmented reality to 98.88% of accuracy rate.

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Section: Realitymentioning

confidence: 99%

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

Prasad

Kumar

Sinha

2014

2014 Seventh International Conference on Contemporary Computing (IC3)

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“…Deller et al [7] made a simple comparison between data acquired from an instrumented glove and a library of static gestures to classify sequences of static gestures and wrist rotations. Their method calculates the distance between the acquired data and the training dataset; if the distance fits within a predefined threshold, the gesture is recognized.…”

Section: Simple Pattern Recognition Techniquesmentioning

confidence: 99%

“…For instance, head movements can change the field of view in an immersive application, and multitouch surfaces [3,4] can provide input/output, allowing users to interact using their fingertips combined with hand movements. Also, free gestures can be interpreted and translated to application events, with few or even no restrictions imposed by former input devices [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of static gesture recognition techniques based on nearest neighbor, neural networks and support vector machines

Savaris

Wangenheim

2010

J Braz Comput Soc

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It is a common behavior for human beings to use gestures as a means of expression, as a complement to speaking, or as a self-contained communication mode. In the field of Human-Computer Interaction, this behavior can be adopted to build alternative interfaces, aiming to ease the relationship between the human element and the computational element. Currently, various gesture recognition techniques are described in the technical literature; however, the validation studies of these techniques are usually performed isolatedly, which complicates comparisons between them. To reduce this gap, this work presents a comparison between three well-established techniques for static gesture recognition, using Nearest Neighbor, Neural Networks, and Support Vector Machines as classifiers. These classifiers evaluate a common dataset, acquired from an instrumented glove, and generate results for precision and performance measurements. The results obtained show that the classifier implemented as a Support Vector Machine presented the best generalization, with the highest recognition rate. In terms of performance, all methods presented evaluation times fast enough to be used interactively. Finally, this work identifies and discusses a set of relevant criteria that must be observed for the training and evaluation steps, and its relation to the final results.

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“…Various related works have been conducted to use hand gestures for human-machine interfaces. These works are divisible into three categories: (A) Glove-type methods [1,2], (B) Electromyography methods [3,4], and (C) Camera methods [5][6][7][8]. However, as Table 1 shows, these methods entail multiple difficulties such as recognition accuracy, interference on activity, troublesome setup processes, mobility, and cost-performance.…”

Section: Introductionmentioning

confidence: 99%

Hand shape classification in various pronation angles using a wearable wrist contour sensor

et al. 2015

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Hand gestures are potentially useful for communications between humans and between a human and a machine. However, existing methods entail several problems for practical use. We have proposed an approach to hand shape recognition based on wrist contour measurement. Especially in this paper, two assignments are addressed. First is the development of a new sensing device in which all elements are installed in a wrist-watch-type device. Second is the development of a new hand shape classifier that can accommodate pronation angle changes. The developed sensing device enables wrist contour data collection under conditions in which the pronation angle varies. The classifier recognizes the hand shape based on statistics produced through data forming and statistics conversion processes. The most important result is that no large difference exists between classification rates that include or those that exclude the independent (preliminary) pronation estimation process using inertia measurement units. This result shows two possible insights: (1) the wrist contour has some features that depend on the hand shape but which do not depend on the pronation angle, or (2) the wrist contour potentially includes pronation angle variation information. These insights indicate the possibility that hand shape can be recognized solely from the wrist contour, even while changing the pronation angle.

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Flexible Gesture Recognition for Immersive Virtual Environments

Cited by 15 publications

References 6 publications

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

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

Comparative evaluation of static gesture recognition techniques based on nearest neighbor, neural networks and support vector machines

Hand shape classification in various pronation angles using a wearable wrist contour sensor

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