A Click Ahead: Real-Time Forecasting of Keyboard and Mouse Actions using RNNs and Computer Vision
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Computer input is more complex than a sequence of single mouse clicks and keyboard presses. We introduce a novel method to identify and represent the user interactions and build a system which predicts - in real-time - the action a user is most likely going to take next. For this, a recurrent neural network (RNN) is trained on a person's usage of the computer. We demonstrate that it is enough to train the RNN on a user's activity over approximately a week to achieve an accuracy of 34.63 almost 500 possible actions. Specific examples for how these predictions may be leveraged to build tools for improving and speeding up workflows of computer users are discussed.
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