Extracting Social Contexts fromBluetooth Detection Logs
Ryo Nishide

Abstract

Kwansei Gakuin University

This project examines the daily detection log ofsurrounding Bluetooth devices in the environment
obtained from the Bluetooth device carried by the user.
The authors show possibilities to determine socialcontexts and changes of surrounding environments byanalyzing the quantity of devices continuously orsimultaneously detected on the detection log.

2-1 Gakuen, Sanda,
Hyogo, 669-1337, Japannishide@kwansei.ac.jp
Tatsuya Ushikoshi
Kwansei Gakuin University
2-1 Gakuen, Sanda,
Hyogo, 669-1337, JapanKeywords

ahk28292@kwansei.ac.jp

Bluetooth device log, ubiquitous computing, context
awareness, social context, everyday computing

Satoshi Nakamura
Kyoto University

ACM Classification Keywords

Yoshida Honmachi Sakyo-ku,

H5.2. Information interfaces and presentation (e.g.,HCI): Miscellaneous.

Kyoto, 606-8501, Japan
nakamura@dl.kuis.kyoto-u.ac.jp
Yasuyuki Kono
Kwansei Gakuin University
2-1 Gakuen, Sanda,Hyogo, 669-1337, Japankono@kwansei.ac.jp

Copyright is held by the author/owner(s).
UbiComp 2009, Sep 30 – Oct 3, 2009, Orlando, FL, USA
ACM XXX-X-XXXXX-XXX-X/XX/XX.

Introduction
This research explores the possibilities of graspingsocial contexts by detecting the surrounding electronicequipments mounted with Bluetooth device, such as
mobile phones, PDAs, laptops and home appliances.
The method consistently detects the surrounding
Bluetooth devices, and generates detection logs. In
contrast to the method which determines user’s
location such as PlaceEngine[1], we attempt to track
the social contexts existing around the user. We cangrasp the social contexts anywhere seamlessly in realtime, without taking actions in collecting data.

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Characteristics of Bluetooth Detection
This paper examines the social context within the 10
meter range of user carrying the class 2 Bluetoothdevice. There are many Bluetooth devices in theenvironment which respond to the inquiry (by sending
their BDA1) automatically. Using these social
circumstances, we try to analyze the surrounding
environment of user.
The features of Bluetooth detection log differ depending
upon the situations of the surroundings. Thus, it ispossible to assume the social contexts by analyzing thedetection logs according to the changes of situations ofsurrounding environments.

Experiment Results

The detection log informs long/short-term detection,appearance/disappearance of multiple BDAs, etc. Thesefeatures indicate the characteristics and changes of thesurrounding environment of user. The detection log
also has possibilities to analyze the user’s activities.
The left figure shows transportation by train duringrush hours. From the log, we can verify threesituations: (i) many people got on/off the train at majorstations; (ii) almost all devices disappeared whenchanging the train; and (iii) several people took thesame train until destination. In the right figure, several
PC/PDAs were detected in 10:00-12:30 during researchmeetings, and one of them had lunch together withuser. In 15:10-16:40, many mobile phones of students
were detected for long-term during class lecture.

Here, we present the observations of Bluetooth
detection log. We have experimented using 5 PDAs
daily, 3 of them for 3 months and 2 of them for 6
months. Fig. 1 shows the detection log of a working
day collected from two colleagues. The time-line isexpressed on the horizontal-axis, and the device ID
assigned in chronological order of the incoming BDA on
the vertical-axis. The mobile phones are colored in red,and PCs and other devices than mobile phones in green.
The following points are verified from detection log:
Quantity or density of people(Change of density of existing devices)
People moving to-and-fro(Flow of existing devices)
People staying in same space (independent from place)(Time length of existing devices)1

Bluetooth Device Address (BDA): Unique ID (MAC address)
assigned to each Bluetooth device during manufacture.

figure 1. Detection log [transportation: left, university: right].

Concluding Remarks
We are currently setting multiple parameters to analyze
the characteristics of detection data. We are also
performing experiments for long-term and short-term
detections, as well as analysis for detection of same
BDA on multiple daily activities.

References[1]

PlaceEngine: http://www.placeengine.com/