Dominant Hand detection using Base Smartphone sensors

There are three primary categories in which user handedness can be classified.All humans can be divided into -dominantly left handed users, dominantly right handed users, and ambidextrous users.Determination of user handedness is generally derived by observing practice of few daily activities such as eating, playing, and writing.However, it is noticed that there are certain tasks, which do not follow the same hand dominance as observed in these activities.This implies that there are certain tasks or activities for which users' preferred handedness is contradictory to the usual preferred handedness or users do not have a preferred/dominant hand.The objective of this research is to determine the hand usage for smartphone interaction by the user and to find if there is any dominance pattern for smartphone interaction.Users have the option of utilizing various smartphone holding patterns to interact with their phones; these patterns include exclusive use of the left hand, exclusive use of the right hand, or the use of both hands simultaneously.This research focuses on understanding and classifying these different smartphone holding patterns via sensor data.The sensor data is collected from three different sensors: an accelerometer, a gyroscope, and an orientation sensor.Features are derived from the collected data, and XGBooster is then used to classify the handedness in three classes (phone used with left hand, phone used with right hand, and phone used with both hands), with an accuracy of 85.04%.