7,300 research outputs found
A preliminary study of a hybrid user interface for augmented reality applications
Augmented Reality (AR) applications are nowadays largely diffused in many fields of use, especially for entertainment, and the market of AR applications for mobile devices grows faster and faster. Moreover, new and innovative hardware for human-computer interaction has been deployed, such as the Leap Motion Controller. This paper presents some preliminary results in the design and development of a hybrid interface for hand-free augmented reality applications. The paper introduces a framework to interact with AR applications through a speech and gesture recognition-based interface. A Leap Motion Controller is mounted on top of AR glasses and a speech recognition module completes the system. Results have shown that, using the speech or the gesture recognition modules singularly, the robustness of the user interface is strongly dependent on environmental conditions. On the other hand, a combined usage of both modules can provide a more robust input
DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation
There is an undeniable communication barrier between deaf people and people
with normal hearing ability. Although innovations in sign language translation
technology aim to tear down this communication barrier, the majority of
existing sign language translation systems are either intrusive or constrained
by resolution or ambient lighting conditions. Moreover, these existing systems
can only perform single-sign ASL translation rather than sentence-level
translation, making them much less useful in daily-life communication
scenarios. In this work, we fill this critical gap by presenting DeepASL, a
transformative deep learning-based sign language translation technology that
enables ubiquitous and non-intrusive American Sign Language (ASL) translation
at both word and sentence levels. DeepASL uses infrared light as its sensing
mechanism to non-intrusively capture the ASL signs. It incorporates a novel
hierarchical bidirectional deep recurrent neural network (HB-RNN) and a
probabilistic framework based on Connectionist Temporal Classification (CTC)
for word-level and sentence-level ASL translation respectively. To evaluate its
performance, we have collected 7,306 samples from 11 participants, covering 56
commonly used ASL words and 100 ASL sentences. DeepASL achieves an average
94.5% word-level translation accuracy and an average 8.2% word error rate on
translating unseen ASL sentences. Given its promising performance, we believe
DeepASL represents a significant step towards breaking the communication
barrier between deaf people and hearing majority, and thus has the significant
potential to fundamentally change deaf people's lives
A software framework for the development of projection-based augmented reality systems
Despite the large amount of methods and applications of augmented reality, there is little homogenization on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more concerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we present a software framework that can be used for the development of AR applications based on camera-projector pairs, that is suitable for both fixed, and nomadic setups.Peer ReviewedPostprint (author's final draft
Exploring the Front Touch Interface for Virtual Reality Headsets
In this paper, we propose a new interface for virtual reality headset: a
touchpad in front of the headset. To demonstrate the feasibility of the front
touch interface, we built a prototype device, explored VR UI design space
expansion, and performed various user studies. We started with preliminary
tests to see how intuitively and accurately people can interact with the front
touchpad. Then, we further experimented various user interfaces such as a
binary selection, a typical menu layout, and a keyboard. Two-Finger and
Drag-n-Tap were also explored to find the appropriate selection technique. As a
low-cost, light-weight, and in low power budget technology, a touch sensor can
make an ideal interface for mobile headset. Also, front touch area can be large
enough to allow wide range of interaction types such as multi-finger
interactions. With this novel front touch interface, we paved a way to new
virtual reality interaction methods
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