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We investigate the utility of mobile accelerometer data to identify human hand gestures and recognize the sign language using Recurrent Neural Networks (RNNs). A set of unsupervised features are learn, to recognize the phrases from American Sign Language (ASL), using Restricted Boltzman Machines (RBM). We validate the efficacy of our method by comparing it with the best performing supervised feature set — the proposed unsupervised features outperform the traditional handcrafted features. We use a labelled dataset of 600 accelerometer readings collected from 50 users to validate our approach.
Text input is a common task in interaction research. The alternative text production systems are typically tested by usability studies and also by standardized performance measurements like word-per-minute, Error Rates etc. Text entry by eye gaze is quite similar to any screen-based text entry technique, such as the on-screen keyboards. The interface is more or less the same, only the interaction technique of pointing and selecting changes as eye gaze is used instead of a stylus or other pointing devices. However, this change in the interaction technique brings a number of design issues that make text entry by eye gaze a unique technique with its own set of research problems.
The concept of CampusConnect (an intranet social networking site) is based and keeping the simple motto – “How can I Help?” and not “What I can Get?” This is to create a positive impact on personal and professional level particularly in the ‘Way of Life in the Campus’, by means of exchange of information or services among individuals, Groups or even like-minded teams who ‘Care and Share’. Most importantly, cultivation of productive relationships within the campus by means of ‘Connecting’ with one another.