Typing Aptitude and Error Pattern Optimization Through Keystroke Dynamics and Recurrent Neural Networks

Typing fluency plays a critical role in daily projects, communication, and procedures. In an increasingly digital world, people are continuously more reliant on typing to navigate day-to-day life, making typing speed and fluidity essential in the modern world. However, typing speeds are quite difficult to raise given the spacing of common characters on the traditional keyboard, and many disabilities present unresolved challenges to efficient keyboard use. To combat this issue, keystroke data was collected and analyzed using recurrent neural networks (RNNs) to identify individual typing proficiencies and deficiencies. To develop the model, sequential timing characteristics were extracted from a timestamped keystroke dataset, standardized, and converted into fixed-length input sequences to train a gated recurrent unit (GRU)-based RNN that can capture temporal relationships in typing behavior. Preliminary results indicate that based on time regularity and error density, the model successfully separates high- and low-proficiency important transitions. Early visualizations show clustered areas with high mistake rates and frequent hesitation, suggesting that the model could help identify the keyboard elements that slow users down the most and cause the most typing errors. These findings show how keystroke dynamics and RNNs can be combined to find significant patterns in each person's typing habits. This framework provides a potential solution to increase speed, accuracy, and comfort, particularly in consulting extreme typing deficiencies. Particularly, physical conditions such as cerebral palsy that make motor constraints that make using a regular keyboard laborious, slow, and prone to mistakes, are contrasted by specific regions of the keyboard where errors and delays are concentrated. A custom keyboard layout informed by data such as this repositions problematic keys to more accessible locations, both alleviating deficiencies and optimizing efficiency.