Entity Recognition via Multimodal Sensor Fusion with Smart Phones

This thesis serves as an exploration that takes the sensors within a cell phone beyond the current state of recognition activities. Current state of the art sensor recognition processes tend to focus on recognizing user activity. Utilizing the same sensors available for user activity classification, this thesis validates the ability to gather data about entities separate from the user carrying the smart phone. With the ability to sense entities, the ability to recognize and classify a multitude of items, situations, and phenomena opens a new realm of possibilities for how devices perceive and react to their environment

SenCity Workshop: Sensing Festivals as Cities

ACM allows authors to post the accepted, peer-reviewed version of their paper on the institutional repository. The published version is available at .In order to sense the mood of a city, we propose first looking at festivals. In festivals such as Glastonbury or Burning Man we see temporary cities where the inhabitants are engaged afresh with their environment and each other. Our position is that not only are there direct equivalences between larger festivals and cities, but in festivals the phenomena are often exaggerated, and the driving impulses often exploratory. These characteristics well suit research into sensing and intervening in the urban experience. To this end, we have built a corpus of sensor and social media data around a 18,000 attendee music festival and are developing ways of analysing and communicating it

Walking in Sync: Two is Company, Three's a Crowd.

Eventual gait synchronization between two individuals while walking and talking with each other has been shown to be an indicator of agreeableness and companionship. The inferred physical signal from this subconscious phenomenon can po-tentially be an indicator of cooperation or relation between two individuals. In this paper we investigate this effect, and whether having a third person actively engaging in the same act or conversation can reduce this synchronization level. Using high frequency accelerometer data from a ded-icated smartphone app, we perform a number of controlled experiments on a number of individuals in different group configuration. Our results bring an interesting insight: it is the non-verbal social signals such as the gaze, head orienta-tion and gestures that is the key factor in synchronization, not necessarily the number or configuration of the walkers. These early results can lead us on detecting relationships between individuals or detecting the group formation and numbers for crowd-sensing applications when only partial data is available. Categories and Subject Descriptors Human-centered computing [Ubiquitous and mobile com-puting]: Empirical studies in ubiquitous and mobile com-putin

Analysing Crowd Behaviours using Mobile Sensing

PhDResearchers 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.UK Defence Science & Technology Laboratory (DSTL

A COMPARATIVE STUDY OF CROWD COUNTING AND PROFILING THROUGH VISUAL AND NON-VISUAL SENSORS

In this paper we present a comparative critical study of visual and non-visual sensors used in crowd behavior analysis. The understanding of crowd has main impact of the analysis how much they support the system is the key factor of the analysis. The surveillance and security prospects are the main feature for crowd that can make system easy and gives better result. The visual sensors that have been widely used are wireless sensor network, computer vision, smart camera, sensor fusion and few more; and the non-visual sensors are regarding the call, IEEE 802, 11 signals measurement, Smart Evactrack, Social Network and Bluetooth etc. This comparative study identified the different analysis of crowd behavior and after analysis we show which technique is better to another one. The smart devices are used now days for surveillance and gives better result in crowd behavior analysis

Location-based Data Analysis of Visitor Structure for Recreational Area Management

This work presents a location-based data analysis framework for profiling visitors structures. In terms of recreational area management, understanding visitors’ structure and popularity is important. Traditionally, visitors monitoring with automatic counting devices has drawbacks of inaccurate visitors counting. In this work, compared to automatic counting devices, we use Wi-Fi tracking as the main method to count visitors, which provides a fairly precise picture of visitor structures. Moreover, we deliver rich analytic functions in this framework and we present the functionality with visitor data collected from Guanyinshan Visitor Center. This framework not only standardizes visitor counting process but also facilitates a profound analysis of visitor structures. Key Words: Guanyinshan Visitor Center, Wi-Fi trackin

A Service-Oriented Approach to Crowdsensing for Accessible Smart Mobility Scenarios

This work presents an architecture to help designing and deploying smart mobility applications. The proposed solution builds on the experience already matured by the authors in different fields: crowdsourcing and sensing done by users to gather data related to urban barriers and facilities, computation of personalized paths for users with special needs, and integration of open data provided by bus companies to identify the actual accessibility features and estimate the real arrival time of vehicles at stops. In terms of functionality, the first "monolithic" prototype fulfilled the goal of composing the aforementioned pieces of information to support citizens with reduced mobility (users with disabilities and/or elderly people) in their urban movements. In this paper, we describe a service-oriented architecture that exploits the microservices orchestration paradigm to enable the creation of new services and to make the management of the various data sources easier and more effective. The proposed platform exposes standardized interfaces to access data, implements common services to manage metadata associated with them, such as trustworthiness and provenance, and provides an orchestration language to create complex services, naturally mapping their internal workflow to code. The manuscript demonstrates the effectiveness of the approach by means of some case studies