GaFinC: Gaze and Finger Control interface for 3D model manipulation in CAD application

Song, Junbong; Cho, Sungmin; Baek, Stephen; Lee, Kunwoo; Bang, Hyunwoo

doi:10.1016/j.cad.2013.08.039

Cited by 37 publications

(35 citation statements)

References 7 publications

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“…CAD/CAE software needs to be engaging to the end-user so that it can consolidate immersion. In the single workstation domain, natural and intuitive interfaces for CAD modelling, such as hand gesture controls, have received a lot of attention recently (Song, Cho, Baek, Lee, & Bang, 2014). Also referred to as virtual reality (VR)-based CAD systems, allow the user to 'grab' an object and manipulate it, as if holding it in his/her hand-physically, providing users with a more natural and intuitive method of interaction.…”

Section: Remote Visualisation Of 3d Modelsmentioning

confidence: 99%

Collaborative CAD/CAE as a cloud service

Zissis

Lekkas

Azariadis

et al. 2016

International Journal of Systems Science: Operations & Logi

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These days manufacturing and design teams continuously collaborate with colleagues across traditional office boundaries and manufacturing floors in an effort to meet with growing demand and competition at a global scale. An essential tool for this industry is computer-aided design and engineering (CAD/CAE) software, which, unfortunately, was never designed to address complex collaboration requirements. Now more than ever, there is a growing need for service-oriented collaborative CAD/CAE solutions, essentially moving away from the high performance single user and machine solutions of the past and towards more portable service-based multi-user systems. The aim of this paper is twofold; first, we examine the domains literature with the goal of documenting a complete set of software requirements, which will map the design of collaborative CAD/CAE solutions, and, second, we present our novel architecture and implementation details of a collaborative CAD/CAE system deployed as a cloud service. Our main focus is on the remote visualisation component of the solution and the practical architectural deployment capable of overcoming software performance, interoperability, modularity, compatibility, scalability and interface interactivity challenges. We validate our architecture in the field by deploying an industry-defined proof-of-concept visualisation system used for the determination and optimisation of the design parameters of professional footwear.

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Section: Remote Visualisation Of 3d Modelsmentioning

confidence: 99%

Collaborative CAD/CAE as a cloud service

Zissis

Lekkas

Azariadis

et al. 2016

International Journal of Systems Science: Operations & Logi

View full text Add to dashboard Cite

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“…To train the system HMM is used resulting 94.8% accuracy. 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.…”

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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“…Dragon Dictation) supplied with sentence syntax analysis and adaptive machine learning. e) Packages supporting Microsoft Kinect and Creative Gesture Camera to control HRT described in our former paper [14][15] are still compatible with HRT and can be used for some special purposes. However, the ultra-fast progress in NUI sensors makes them not competitive with latest devices.…”

Section: Nui Hardwarementioning

confidence: 99%

“…Such multi-modal approach (exploited e.g. in CAD system [14]) can significantly speed up the dwell-time between the action of the operator and the reaction of the system.…”

Section: Introductionmentioning

confidence: 99%

Natural user interface as a supplement of the holographic Raman tweezers

et al. 2014

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Holographic Raman tweezers (HRT) manipulates with microobjects by controlling the positions of multiple optical traps via the mouse or joystick. Several attempts have appeared recently to exploit touch tablets, 2D cameras or Kinect game console instead. We proposed a multimodal "Natural User Interface" (NUI) approach integrating hands tracking, gestures recognition, eye tracking and speech recognition. For this purpose we exploited "Leap Motion" and "MyGaze" low-cost sensors and a simple speech recognition program "Tazti". We developed own NUI software which processes signals from the sensors and sends the control commands to HRT which subsequently controls the positions of trapping beams, micropositioning stage and the acquisition system of Raman spectra. System allows various modes of operation proper for specific tasks. Virtual tools (called "pin" and "tweezers") serving for the manipulation with particles are displayed on the transparent "overlay" window above the live camera image. Eye tracker identifies the position of the observed particle and uses it for the autofocus. Laser trap manipulation navigated by the dominant hand can be combined with the gestures recognition of the secondary hand. Speech commands recognition is useful if both hands are busy. Proposed methods make manual control of HRT more efficient and they are also a good platform for its future semiautomated and fully automated work.

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GaFinC: Gaze and Finger Control interface for 3D model manipulation in CAD application

Cited by 37 publications

References 7 publications

Collaborative CAD/CAE as a cloud service

Collaborative CAD/CAE as a cloud service

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

Natural user interface as a supplement of the holographic Raman tweezers

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