Smartphone analysis and optimization based on user activity recognition

Behavior of smartphone systems is highly influenced by user interactions, such as `zooming´ and `scrolling´, which determine the execution phases within applications and lead to different power and performance demands. Current power and thermal management algorithms are agnostic to these behaviors. We propose a novel user activity recognition framework that enables user activity-aware system decisions. The proposed framework carefully monitors system events initiated by user interactions and identifies the current user activity based on an online activity model. We implemented the proposed framework in Android platform, and tested it on Qualcomm MDP 8660 smartphone. To show the practical value of our recognition strategy, we design effective power and thermal management policies that adapt system settings to user activity changes. Our experimental results using 10 real mobile applications show that the proposed proactive management technique can reduce the CPU energy by up to 28% while meeting a given thermal constraint.