| dc.description.abstract | Researchers have examined crowd behaviour in the past by employing a variety of methods
including ethnographic studies, computer vision techniques and manual annotation-based
data analysis. However, because of the resources to collect, process and analyse data, it
remains difficult to obtain large data sets for study. Mobile phones offer easier means for
data collection that is easy to analyse and can preserve the user’s privacy. The aim of this
thesis is to identify and model different qualities of social interactions inside crowds using
mobile sensing technology. This Ph.D. research makes three main contributions centred
around the mobile sensing and crowd sensing area.
Firstly, an open-source licensed mobile sensing framework is developed, named SensingKit,
that is capable of collecting mobile sensor data from iOS and Android devices,
supporting most sensors available in modern smartphones. The framework has been evaluated
in a case study that investigates the pedestrian gait synchronisation phenomenon.
Secondly, a novel algorithm based on graph theory is proposed capable of detecting
stationary social interactions within crowds. It uses sensor data available in a modern
smartphone device, such as the Bluetooth Smart (BLE) sensor, as an indication of user
proximity, and accelerometer sensor, as an indication of each user’s motion state.
Finally, a machine learning model is introduced that uses multi-modal mobile sensor
data extracted from Bluetooth Smart, accelerometer and gyroscope sensors. The validation
was performed using a relatively large dataset with 24 participants, where they
were asked to socialise with each other for 45 minutes. By using supervised machine
learning based on gradient-boosted trees, a performance increase of 26.7% was achieved
over a proximity-based approach. Such model can be beneficial to the design and implementation
of in-the-wild crowd behavioural analysis, design of influence strategies, and
algorithms for crowd reconfiguration. | en_US |
