Keystroke Dynamics-Based Analysis and Classification of Hand Posture Using Machine Learning Techniques

Keystroke dynamics, sometimes known as typing biometrics, is an automated method of recognizing or verifying a person's identity based on the style and rhythm of their keyboard strokes. It alludes to the precise timing data that shows when each key was pressed and when it was released during keyboard typing. Keystroke Dynamics assists in identifying a specific person's hand biometric template. This research uses a wide range of dwell time and flight time attributes to ascertain the hand posture of a specific person. To determine a user's hand posture at any given time, an Android application was created to record about 13 distinctive and unquestionably important attributes. Through this application, information was gathered by having users participate in a typing session. A variety of keystroke-related data, including Pressure, Finger Area, Uptime, and Downtime for each key, and motion-based data, including RawX, RawY, GravityX, GravityY, and GravityZ, were collected. Additionally, to achieve higher levels of accuracy, multiple Machine Learning models were used including ensemble classification methods like Bagging and Boosting to achieve conclusive results. It was observed that the Random Forest classifier obtained the highest accuracy score of 97.30%. The model was integrated with a mobile application and was utilized to identify the hand involved in the typing process. This work can be extended to include the field of surveillance, Multi-factor Authentication (MFA), and to help improve the one-hand mode layout.