Abstract
By developing awareness of smartphone activities that the user is performing on their smartphone, such as scrolling feeds, typing and watching videos, we can develop application features that are beneficial to the users, such as personalization. It is currently not possible to access real-time smartphone activities directly, due to standard smartphone privileges and if internal movement sensors can detect them, there may be implications for access policies. Our research seeks to understand whether the sensor data from existing smartphone inertial measurement unit (IMU) sensors (triaxial accelerometers, gyroscopes and magnetometers) can be used to classify typical human smartphone activities. We designed and conducted a study with human participants which uses an Android app to collect motion data during scrolling, typing and watching videos, while walking or seated and the baseline of smartphone non-use, while sitting and walking. We then trained a machine learning (ML) model to perform real-time activity recognition of those eight states. We investigated various algorithms and parameters for the best accuracy. Our optimal solution achieved an accuracy of 78.6% with the Extremely Randomized Trees algorithm, data sampled at 50 Hz and 5-s windows. We conclude by discussing the viability of using IMU sensors to recognize common smartphone activities.
Highlights
Human activity tracking using smartphone sensors has been a key concern since smartphones have been released, in part due to smartphones’ physical size, ubiquity, unobtrusiveness and ease of use
If a news or social media app uses inertial measurement unit (IMU) sensors for this, they can present the user with more personalized content
We conducted a study with human participants, using an Android app which we developed to collect labelled IMU sensor data while they were carrying out the activities which we aimed to classify
Summary
Human activity tracking using smartphone sensors has been a key concern since smartphones have been released, in part due to smartphones’ physical size, ubiquity, unobtrusiveness and ease of use. Whole-body human activities such as walking and running have been investigated but physically smaller human activities such as reading or typing have not been explored. News and social media applications log when users watch videos or read in order to present personalized content which is more likely to be engaging to each user [4]. If a news or social media app uses inertial measurement unit (IMU) sensors for this, they can present the user with more personalized content (i.e., more videos or more text, depending on what the user uses and likes)
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