Dynamic Cat-Boost Enabled Keystroke Analysis for User Stress Level Detection

The impact of digital gadgets is enormous in the current Internet world because of the easy accessibility, flexibility and time-saving benefits for the consumers. The number of computer users is increasing every year. Meanwhile, the time spent and the computers also increased. Computer users browse the internet for various information gathering and stay on the internet for a long time without control. Nowadays working people from home also spend time with the smart devices, computers, and laptops, for a longer duration to complete professional work, personal work etc. the proposed study focused on deriving the impact factors of Smartphones by analyzing the keystroke dynamics Based on the usage pattern of keystrokes the system evaluates the stress level detection using machine learning techniques. In the proposed study keyboard users are intended for testing purposes. Volunteers of 200 members are collectively involved in generating the test dataset. They are allowed to sit for a certain frame of time to use the laptop in the meanwhile the keystroke of the Mouse and keyboard are recorded. The system reads the dataset and trains the model using the Dynamic Cat-Boost algorithm (DCB), which acts as the classification model. The evaluation metrics are framed by calculating Euclidean distance (ED), Manhattan Distance (MahD), Mahalanobis distance (MD) etc. Quantitative measures of DCB are framed through Accuracy, precision and F1Score.