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Probabilistic mining of socio-geographic routines from mobile phone data

Probabilistic mining of socio-geographic routines from mobile phone data
Probabilistic mining of socio-geographic routines from mobile phone data
There is relatively little work on the investigation of large-scale human data in terms of multimodality for human activity discovery. In this paper, we suggest that human interaction data, or human proximity, obtained by mobile phone Bluetooth sensor data, can be integrated with human location data, obtained by mobile cell tower connections, to mine meaningful details about human activities from large and noisy datasets. We propose a model, called bag of multimodal behavior, that integrates the modeling of variations of location over multiple time-scales, and the modeling of interaction types from proximity. Our representation is simple yet robust to characterize real-life human behavior sensed from mobile phones, which are devices capable of capturing large-scale data known to be noisy and incomplete. We use an unsupervised approach, based on probabilistic topic models, to discover latent human activities in terms of the joint interaction and location behaviors of 97 individuals over the course of approximately a 10-month period using data from MIT's Reality Mining project. Some of the human activities discovered with our multimodal data representation include “going out from 7 pm-midnight alone” and “working from 11 am-5 pm with 3-5 other people,” further finding that this activity dominantly occurs on specific days of the week. Our methodology also finds dominant work patterns occurring on other days of the week. We further demonstrate the feasibility of the topic modeling framework for human routine discovery by predicting missing multimodal phone data at specific times of the day.
746 - 755
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Gatica-Perez, Daniel
8eaa6830-e159-470f-85e1-fb88ea8846fa
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Gatica-Perez, Daniel
8eaa6830-e159-470f-85e1-fb88ea8846fa

Farrahi, Katayoun and Gatica-Perez, Daniel (2010) Probabilistic mining of socio-geographic routines from mobile phone data. IEEE Journal of Selected Topics in Signal Processing, 4 (4), 746 - 755. (doi:10.1109/JSTSP.2010.2049513).

Record type: Article

Abstract

There is relatively little work on the investigation of large-scale human data in terms of multimodality for human activity discovery. In this paper, we suggest that human interaction data, or human proximity, obtained by mobile phone Bluetooth sensor data, can be integrated with human location data, obtained by mobile cell tower connections, to mine meaningful details about human activities from large and noisy datasets. We propose a model, called bag of multimodal behavior, that integrates the modeling of variations of location over multiple time-scales, and the modeling of interaction types from proximity. Our representation is simple yet robust to characterize real-life human behavior sensed from mobile phones, which are devices capable of capturing large-scale data known to be noisy and incomplete. We use an unsupervised approach, based on probabilistic topic models, to discover latent human activities in terms of the joint interaction and location behaviors of 97 individuals over the course of approximately a 10-month period using data from MIT's Reality Mining project. Some of the human activities discovered with our multimodal data representation include “going out from 7 pm-midnight alone” and “working from 11 am-5 pm with 3-5 other people,” further finding that this activity dominantly occurs on specific days of the week. Our methodology also finds dominant work patterns occurring on other days of the week. We further demonstrate the feasibility of the topic modeling framework for human routine discovery by predicting missing multimodal phone data at specific times of the day.

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e-pub ahead of print date: 3 May 2010
Published date: August 2010

Identifiers

Local EPrints ID: 419924
URI: https://eprints.soton.ac.uk/id/eprint/419924
PURE UUID: ff7465cc-c61e-40db-8ff6-91783c74d08b

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Date deposited: 23 Apr 2018 16:30
Last modified: 13 Mar 2019 18:37

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