Detecting User Reading Behaviour Using Smartphone Sensors

Abstract

There are many different ways to rate mobile content in the form of various explicit user feedback e.g. like buttons, thumbs up and thumbs down, star ratings as well as there are ways to analyse usage statistics of applications on using mobile analytics tools. Implicit feedback enables to collect more data for getting better insight of content usage and user behaviour. In recent years many works have been conducted in order to classify activities using smartphones. Previous works have shown that sensor-based activity recognition on smartphones is feasible. Yet previous works have not classified reading activity on smartphones. This work proposes one possible way to classify this activity with high accuracy. Classifying reading activity provides possibility to have more precise estimates on mobile content usage statistics, by utilizing sensorand visual-based activity recognition techniques. A set of mobile applications was developed to facilitate data collection and labelling. Accelerometer and gyroscope data was collected from 35 different subjects, after cleaning data 4438 sample readings were left. A neural network was trained on 80% of data and 94% accuracy was reached on classifying reading activity using a smartphone. The results show that classifying reading activity using accelerometer and gyroscope data is possible with high degree of accuracy. We provide Android application source code along with neural network training implementation accompanied by training data in a Git repository