Stress Detection in Computer Users From Keyboard and Mouse Dynamics
Product Description
Alternate Title: Dynamic Cat Boost enabled Stress level detection system using keyboard and mouse striking style
Abstract
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. For various information gathering, computer users browse the internet and stay in the internet for longer time without control. Nowadays working people from home also spend time with the smart devices, computers, laptops, for 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 certain frame of time to use the Laptop in the mean while keystroke of Mouse and keyboard is recorded. The system read the dataset and train the model using Dynamic Cat-Boost algorithm (DCB), that act as the classification model here. The evaluation metrics are framed by calculating Euclidean distance(ED), Manhattan Distance (MahD), and Mahalanobis distance(MD) etc. Quantitative measures of DCB is framed through Accuracy, precision and F1Score.
Proposed system
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 certain frame of time to use the Laptop in the mean while keystroke of Mouse and keyboard is recorded. The system read the dataset and train the model using Dynamic Cat-Boost algorithm (DCB), that act as the classification model here. The evaluation metrics are framed by calculating Euclidean distance(ED), Manhattan Distance (MahD), and Mahalanobis distance(MD) etc. Quantitative measures of DCB is framed through Accuracy, precision and F1Score.
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Mini Projects - Hardware includes
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The Delivery time for Hardware Mini projects is 7-8 working days.