The security of modern smartphones is related to the combination ofContinuous Authenticationapproaches,Touch events, and Human Activities. The approaches ofContinuous Authentication, Touch Events, and Human Activitiesare silent to the user but are a great source of data for Machine Learning Algorithms. This work aims to develop a method for continuous authentication while the user is sitting and scrolling documents on the smartphone. Touch Events and Smartphone Sensor Features (from the well-knownH-MOG Dataset) were used with the addition, for each sensor, of the feature calledSignal Vector Magnitude.SeveralMachine Learning Modelshave been considered with different experiment setups,1-class,and2-class,for evaluation. The results show that the 1-class SVM achieves an accuracy of 98.9% and an F1-score of 99.4%, considering the selected features and the featureSignal Vector Magnitudevery significant.