CNN-Based Camera-less User Attention Detection for Smartphone Power Management

The many sensors hosted by mobile electronic devices are commonly used to recognize user activities and context, in order to provide new functionalities, such as tracking physical activity and sleep cycles. Despite its potential, such context recognition is only employed for power management purposes in very specific scenarios (e.g. in-pocket detection). In this work we present a novel context recognition system able to reliably identify whether a mobile device is not being looked at, and to consequently trigger power management actions such as turning off the display and moving to suspended mode. Our method takes as input the readings from common low-power sensors present in virtually all mobile devices and classifies them using a Convolutional Neural Network. Most importantly, the power-hungry camera sub-system is not used, resulting in an extremely energy-efficient detection strategy. Results show that our system is able to identify scenarios in which a device is not being used with 95.6% accuracy, thus reducing the energy overheads by 91% compared to a standard timeout-based power management and by 58% compared to a system relying on the camera