LIPS: Lifestyle learning via mobile phone sensing

Xiang Sheng, Jian Tang, Jing Wang, Teng Li, Guoliang Xue, Dejun Yang

Research output: Contribution to journalConference Articlepeer-review

2 Scopus citations


In this paper, we propose to learn LIfestyles of mobile users via mobile Phone Sensing (LIPS), and we develop a system and algorithms to realize this idea. First, we present the workflow and architecture of our system, LIPS. Combining both unsupervised and supervised learning, we propose a hybrid scheme for lifestyle learning, which consists of two parts: characterization and prediction. Specifically, we present a two-stage algorithm to characterize the lifestyle of a mobile user using Places of Interest (PoIs), which leverages two different algorithms for coarse- grained and fine-grained clustering in two stages respectively. Based on discovered PoIs, we present a method to build a model to predict his/her future activities using a supervised classification algorithm. In addition, we present an adaptive sampling algorithm for improving energy efficiency, which leverages both the discovered PoIs and the lifestyle model for adaptively controlling the sampling rate. We implemented the proposed system and algorithms based on the Android platform. We have validated and evaluated LIPS via extensive field tests carried out for over 1.5 months in 6 cities of USA. The experimental results show that LIPS can 1) well discover PoIs of mobile users, 2) precisely predict their future activities, and 3) achieve significant energy savings (compared to periodic sampling).

Original languageEnglish (US)
Article number7841959
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
StatePublished - 2016
Event59th IEEE Global Communications Conference, GLOBECOM 2016 - Washington, United States
Duration: Dec 4 2016Dec 8 2016


  • Energy Efficiency
  • Human-Centric Sensing
  • Mobile Computing
  • Mobile Phone Sensing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing


Dive into the research topics of 'LIPS: Lifestyle learning via mobile phone sensing'. Together they form a unique fingerprint.

Cite this