BTP: A Bedtime Predicting Algorithm via Smartphone Screen Status

Abstract

For smartphone service providers, it is of vital importance to recognize characteristics of customers. The process of recognizing these characteristics is generally referred to as user profile, which provides knowledge basis for business decisions, enables intelligent services, and brings unique competitiveness. As a basic component of user profile, bedtime could reflect lifestyle, health condition, and occupation of people. This paper presents a flexible algorithm named BTP (Bedtime Prediction), which is designed for predicting wake time and bedtime by analysing screen status of smartphone. BTP first collects screen status log data of user’s smartphone and conducts preprocessing with a series of auxiliary user profiles. Then, it detects and records users’ wake time and bedtime of one day by searching and combining major screen extinguish periods in the past 24 hours. Finally, BTP predicts future bedtime by matching current screen status sequence with all historical records. By applying BTP, most of night and morning scenario-based applications could provide more considerate services, rather than following fixed execution time like alarm clock. Experiments on practical applications prove that BTP can effectively predict wake time and bedtime without applying complicated machine learning algorithms or uploading data to server.