Detecting User Reading Behaviour Using Smartphone Sensors
Date
2015
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
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