Text-entry aims to provide an effective and efficient pathway for humans to deliver their messages to computers. With the advent of mobile computing, the recent focus of text-entry research has moved from physical keyboards to soft keyboards. Current soft keyboards, however, increase the typo rate due to lack of tactile feedback and degrade the usability of mobile devices due to their large portion on screens. To tackle these limitations, we propose a fully imaginary keyboard (I-Keyboard) with a deep neural decoder (DND). The invisibility of I-Keyboard maximizes the usability of mobile devices and DND empowered by a deep neural architecture allows users to start typing from any position on the touch screens at any angle. To the best of our knowledge, the eyes-free ten-finger typing scenario of I-Keyboard which does not necessitate both a calibration step and a predefined region for typing is first explored in this work. For the purpose of training DND, we collected the largest user data in the process of developing I-Keyboard. We verified the performance of the proposed I-Keyboard and DND by conducting a series of comprehensive simulations and experiments under various conditions. I-Keyboard showed 18.95 and 4.06 increases in typing speed (45.57 WPM) and accuracy (95.84 ), respectively over the baseline.
First of all, gesture-based text-entry allows drawing-like typing @cite_17 @cite_26 . Drawing-like typing removes the need for localizing each key position and users can start drawing from any place on the screen in an eyes-free manner. Though gesture-based text-entry offers concise eyes-free typing interfaces, it requires gesture recognition algorithms, which can hardly achieve a high accuracy @cite_4 . Gesture variability among users and similarities between gestures for each key cause ambiguity that increases the inherent difficulty of sequence classification @cite_21 . In addition, gesture-based text-entry takes longer time than other text-entry methods since each key involves a gesture rather than a touch or a key press. The proposed I-Keyboard targets a more tractable decoding problem and utilizes deep learning techniques to successfully deal with the variability among users.
@cite_26 @cite_4 @cite_21 @cite_17
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In this paper, we propose a complete gesture recognition framework based on maximum cosine similarity and fast nearest neighbor (NN) techniques, which offers high-recognition accuracy and great computational advantages for three fundamental problems of gesture recognition: 1) isolated recognition; 2) gesture verification; and 3) gesture spotting on continuous data streams. To support our arguments, we provide a thorough evaluation on three large publicly available databases, examining various scenarios, such as noisy environments, limited number of training examples, and time delay in system’s response. Our experimental results suggest that this simple NN-based approach is quite accurate for trajectory classification of digits and letters and could become a promising approach for implementations on low-power embedded systems. Recognizing human actions in 3-D video sequences is an important open problem that is currently at the heart of many research domains including surveillance, natural interfaces and rehabilitation. However, the design and development of models for action recognition that are both accurate and efficient is a challenging task due to the variability of the human pose, clothing and appearance. In this paper, we propose a new framework to extract a compact representation of a human action captured through a depth sensor, and enable accurate action recognition. The proposed solution develops on fitting a human skeleton model to acquired data so as to represent the 3-D coordinates of the joints and their change over time as a trajectory in a suitable action space. Thanks to such a 3-D joint-based framework, the proposed solution is capable to capture both the shape and the dynamics of the human body, simultaneously. The action recognition problem is then formulated as the problem of computing the similarity between the shape of trajectories in a Riemannian manifold. Classification using k-nearest neighbors is finally performed on this manifold taking advantage of Riemannian geometry in the open curve shape space. Experiments are carried out on four representative benchmarks to demonstrate the potential of the proposed solution in terms of accuracy latency for a low-latency action recognition. Comparative results with state-of-the-art methods are reported. KeyScretch is a text entry method for devices equipped with touch-screens, based on a menu-augmented soft keyboard. In these keyboards, a menu containing a small number of frequent characters is shown, while a key is pressed, allowing further character entry by menu selection. KeyScretch improves the previously studied menu-based methods by enabling the interpretation of compound strokes, which allow the input of text chunks longer than two characters. The performance of the method is analyzed on different kinds of touch-screens: First, we present a 25-session user study on a stylus-based device, showing that an instance of the method optimized for Italian can be learned in a reasonable time by the users and significantly outperforms the traditional method based on the tapping interaction. Then, we define and validate a model for predicting expert text entry rates on finger-based devices. The predicted rates for instances of KeyScretch optimized for different Western languages vary from about 44-50 words min on the Qwerty layout, enabling improvements in the range of 30-49 as compared with the traditional method.