A computing device may detect user input, such as finger movements resembling typing on an invisible virtual keyboard in the air or on any surface, to enable typing. The computing device may use sensors (e.g., accelerometers, cameras, piezoelectric sensors, etc.) to detect the user’s finger movements, such as the user’s fingers moving through the air and/or contacting a surface. The computing device may then decode (or, in other words, convert, interpret, analyze, etc.) the detected finger movements to identify corresponding inputs representative of characters (e.g., alphanumeric characters, national characters, special characters, etc.). To reduce input errors, the computing device may decode the detected finger movements, at least in part, based on contextual information, such as preceding characters, words, and/or the like entered via previously detected user inputs. Similarly, the computing device may apply machine learning techniques and adjust parameters, such as a signal-to-noise ratio, to improve the accuracy of input-entry. In some examples, the computing device may implement specific recognition, prediction, and correction algorithms to improve the accuracy of input-entry. In this way, the computing device may accommodate biasing in finger movements that may be specific to a user entering the input.
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Zhai, Shumin and Zhang, Mingrui, "TWO-HANDED TYPING METHOD ON AN ARBITRARY SURFACE", Technical Disclosure Commons, (March 12, 2021)