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.

中文翻译：

---

使用惯性传感器进行实时智能手机活动分类-识别坐着或走路时的滚动，打字和观看视频。

通过提高用户在智能手机上执行的智能手机活动（例如，滚动供稿，键入和观看视频）的意识，我们可以开发对用户有益的应用程序功能，例如个性化。由于标准的智能手机特权，当前无法直接访问实时智能手机活动，并且如果内部移动传感器可以检测到它们，则可能会对访问策略产生影响。我们的研究旨在了解现有智能手机惯性测量单元（IMU）传感器（三轴加速度计，陀螺仪和磁力计）的传感器数据是否可用于对典型的人类智能手机活动进行分类。我们设计并与人类参与者进行了一项研究，该研究使用Android应用程序在滚动，键入和观看视频时收集运动数据，走路或坐着时，以及坐在和走路时不使用智能手机的基线。然后，我们训练了机器学习（ML）模型来执行对这八个状态的实时活动识别。我们研究了各种算法和参数以实现最佳准确性。我们的最佳解决方案使用“极端随机树”算法以50 Hz和5秒窗口采样的数据达到了78.6％的精度。最后，我们讨论了使用IMU传感器识别常见智能手机活动的可行性。极端随机树算法的6％，数据以50 Hz和5秒窗口采样。最后，我们讨论了使用IMU传感器识别常见智能手机活动的可行性。极端随机树算法的6％，数据以50 Hz和5秒窗口采样。最后，我们讨论了使用IMU传感器识别常见智能手机活动的可行性。