Discovering Human Routines from Cell Phone Data with Topic Models
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Inspired by the recent success of RGB-D cameras, we propose the enrichment of RGB data with an additional "quasi-free" modality, namely, the wireless signal (e.g., wifi or Bluetooth) emitted by individuals' cell phones, referred to as RGB-W. The received s ...
We present a new approach to address the problem of large sequence mining from big data. The particular problem of interest is the effective mining of long sequences from large-scale location data to be practical for Reality Mining applications, which suff ...
By exploiting built-in sensors, mobile smartphone have become attractive options for large-scale sensing of human behavior as well as social interaction. In this paper, we present a new probabilistic model to analyze longitudinal dynamic social networks cr ...
Mining patterns of human behavior from large-scale mobile phone data has potential to understand certain phenomena in society. The study of such human-centric massive datasets requires new mathematical models. In this paper, we propose a probabilistic topi ...
This report presents a study on assisting users in building queries to perform real-time searches in a news and social media monitoring system. The system accepts complex queries, and we assist the user by suggesting related keywords or entities. We do thi ...
Large population displacements are usually observed after nature disasters. The best approximations to real world population movement in such a short temporal scale are the users' movements patterns derived from the cell phone usage data. However, due to a ...
We propose a distributed least-mean squares (LMS) procedure based on a diffusion strategy for localization and tracking of mobile terminals in cellular networks. In the proposed algorithm, collaborating base stations measure two sets of parameters, namely, ...
As we live our daily lives, our surroundings know about it. Our surroundings consist of people, but also our electronic devices. Our mobile phones, for example, continuously sense our movements and interactions. This socio-geographic data could be continuo ...
In this thesis, we address the analysis of activities from long term data logs with an emphasis on video recordings. Starting from simple words from video, we progressively build methods to infer higher level scene semantics. The main strategies used to ac ...
In this work we discover the daily location-driven routines which are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines which characterize both individual and group behaviors ...
