Location-based technologies for learning

Emerging technologies for learning report - Article exploring location based technologies and their potential for educatio

Probabilistic modelling and inference of human behaviour from mobile phone time series

With an estimated 4.1 billion subscribers around the world, the mobile phone offers a unique opportunity to sense and understand human behaviour from location, co-presence and communication data. While the benefit of modelling this unprecedented amount of data is widely recognised, a number of challenges impede the development of accurate behaviour models. In this thesis, we identify and address two modelling problems and show that their consideration improves the accuracy of behaviour inference. We first examine the modelling of long-range dependencies in human behaviour. Human behaviour models only take into account short-range dependencies in mobile phone time series. Using information theory, we quantify long-range dependencies in mobile phone time series for the first time, demonstrate that they exhibit periodic oscillations and introduce novel tools to analyse them. We further show that considering what the user did 24 hours earlier improves accuracy when predicting user behaviour five hours or longer in advance. The second problem that we address is the modelling of temporal variations in human behaviour. The time spent by a user on an activity varies from one day to the next. In order to recognise behaviour patterns despite temporal variations, we establish a methodological connection between human behaviour modelling and biological sequence alignment. This connection allows us to compare, cluster and model behaviour sequences and introduce novel features for behaviour recognition which improve its accuracy. The experiments presented in this thesis have been conducted on the largest publicly available mobile phone dataset labelled in an unsupervised fashion and are entirely repeatable. Furthermore, our techniques only require cellular data which can easily be recorded by today's mobile phones and could benefit a wide range of applications including life logging, health monitoring, customer profiling and large-scale surveillance

Smart technologies and beyond: exploring how a smart band can assist in monitoring children’s independent mobility & well-being

The problem which is being investigated through this thesis is not having a device(s) or method(s) which are appropriate for monitoring a child’s vital and tracking a child’s location. This aspect is being explored by other researchers which are yet to find a viable solution. This work focuses on providing a solution that would consider using the Internet of Things for measuring and improving children’s health. Additionally, the focus of this research is on the use of technology for health and the needs of parents who are concerned about their child’s physical health and well-being. This work also provides an insight into how technology is used during the pandemic. This thesis will be based on a mixture of quantitative and qualitative research, which will have been used to review the following areas covering key aspects and focuses of this study which are (i) Children’s Independent Mobility (ii) Physical activity for children (iii) Emotions of a child (iv) Smart Technologies and (v) Children’s smart wearables. This will allow a review of the problem in detail and how technology can help the health sector, especially for children. The deliverable of this study is to recommend a suitable smart band device that enables location tracking of the child, activity tracking as well as monitoring the health and wellbeing of the child. The research also includes an element of practical research in the form of (i) Surveys, the use of smart technology and a perspective on the solution from parents. (ii) Focus group, in the form of a survey allowing opinions and collection of information on the child and what the parents think of smart technology and how it could potentially help with their fears. (iii) Observation, which allows the collection of data from children who were given six activities to conduct while wearing the Fitbit Charge HR. The information gained from these elements will help provide guidelines for a proposed solution. In this thesis, there are three frameworks which are about (i) Research process for this study (ii) Key Performance Indicators (KPIs) which are findings from the literature review and (iii) Proposed framework for the solution, all three combined frameworks can help health professionals and many parents who want an efficient and reliable device, also deployment of technologies used in the health industry for children in support of independent mobility. Current frameworks have some considerations within the technology and medical field but were not up to date with the latest elements such as parents fears within today’s world and the advanced features of technology

A Mobile Cyber-Physical System Framework for Aiding People with Visual Impairment

It is a challenging problem for researchers and engineers in the assistive technology (AT) community to provide suitable solutions for visually impaired people (VIPs) through AT to meet orientation, navigation and mobility (ONM) needs. Given the spectrum of assistive technologies currently available for the purposes of aiding VIPs with ONM, our literature review and survey have shown that there is a reluctance to adopt these technological solutions in the VIP community. Motivated by these findings, we think it critical to re-examine and rethink the approaches that have been taken. It is our belief that we need to take a different and innovative approach to solving this problem. We propose an integrated mobile cyber-physical system framework (MCPSF) with an \u27agent\u27 and a \u27smart environment\u27 to address VIP\u27s ONM needs in urban settings. For example, one of the essential needs for VIPs is to make street navigation easier and safer for them as pedestrians. In a busy city neighborhood, crossing a street is problematic for VIPs: knowing if it is safe; knowing when to cross; and being sure to remain on path and not collide or interfere with objects and people. These remain issues keeping VIPs from a truly independent lifestyle. In this dissertation, we propose a framework based on mobile cyber-physical systems (MCPS) to address VIP\u27s ONM needs. The concept of mobile cyber-physical systems is intended to bridge the physical space we live in with a cyberspace filled with unique information coming from IoT devices (Internet of Things) which are part of Smart City infrastructure. The devices in the IoT may be embedded in different kinds of physical structures. People with vision loss or other special needs may have difficulties in comprehending or perceiving signals directly in the physical space, but they can make such connections in cyberspace. These cyber connections and real-time information exchanges will enable and enhance their interactions in the physical space and help them better navigate through city streets and street crossings. As part of the dissertation work, we designed and implemented a proof of concept prototype with essential functions to aid VIP’s for their ONM needs. We believe our research and prototype experience opened a new approach to further research areas that might enhance ONM functions beyond our prototype with potential commercial product development

Developing a Methodology for Monitoring Personal Exposure to Particulate Matter in a Variety of Microenvironments

Adverse health effects from exposure to air pollution, although at present only partly understood, are a global challenge and of widespread concern. Quantifying human exposure to air pollutants is challenging, as ambient concentrations of air pollutants at potentially harmful levels are ubiquitous and subject to high spatial and temporal variability. At the same time, individuals have their very own unique activity-patterns. Hence exposure results from intertwined relationships between environmental and human systems add complexity to the assessment process. It is essential to develop a deeper understanding of individual exposure pathways and situations occurring in people’s everyday lives. This is important especially with regard to exposure and health impact assessment which provide the basis for public health advice and policy development. This thesis describes the development and application of a personal monitoring method to assess exposure to fine particulate matter in a variety of microenvironments. Tools and methods applied are tested with respect to feasibility, intrusiveness, performance and potential for future applications. The development of the method focuses on the application in everyday environments and situations in an attempt to capture as much of the total exposure as possible, across a complete set of microenvironments. Seventeen volunteers took part in the pilot study, collected data and provided feedback on methodology and tools applied. The low-cost particle counter applied showed good agreement with reference instruments when studied in two different environments. Based on the assessment of the two instruments functions to derive particle mass concentration from the original particle number counts have been defined. The application of the devices and tools received positive feedback from the volunteers. Limitations are mainly related to the non-weatherproof design of the particle counter. The collection of time-activity patterns with GPS and time-activity diaries is challenging and requires careful processing. Resulting personal exposure profiles highlight the influence of individual activities and contextual factors. Highest concentrations were measured in indoor environments where people also spent the majority of time. Differences between transport modes as well as between urban and rural areas were identified

Organising and structuring a visual diary using visual interest point detectors

As wearable cameras become more popular, researchers are increasingly focusing on novel applications to manage the large volume of data these devices produce. One such application is the construction of a Visual Diary from an individual’s photographs. Microsoft’s SenseCam, a device designed to passively record a Visual Diary and cover a typical day of the user wearing the camera, is an example of one such device. The vast quantity of images generated by these devices means that the management and organisation of these collections is not a trivial matter. We believe wearable cameras, such as SenseCam, will become more popular in the future and the management of the volume of data generated by these devices is a key issue. Although there is a significant volume of work in the literature in the object detection and recognition and scene classification fields, there is little work in the area of setting detection. Furthermore, few authors have examined the issues involved in analysing extremely large image collections (like a Visual Diary) gathered over a long period of time. An algorithm developed for setting detection should be capable of clustering images captured at the same real world locations (e.g. in the dining room at home, in front of the computer in the office, in the park, etc.). This requires the selection and implementation of suitable methods to identify visually similar backgrounds in images using their visual features. We present a number of approaches to setting detection based on the extraction of visual interest point detectors from the images. We also analyse the performance of two of the most popular descriptors - Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF).We present an implementation of a Visual Diary application and evaluate its performance via a series of user experiments. Finally, we also outline some techniques to allow the Visual Diary to automatically detect new settings, to scale as the image collection continues to grow substantially over time, and to allow the user to generate a personalised summary of their data

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity