Enhancing Personal Informatics Through Social Sensemaking

Personal informatics practices are increasingly common, with a range of consumer technologies available to support, largely individual, interactions with data (e.g., performance measurement and activity/health monitoring). In this paper, we explore the concept of social sensemaking. In contrast to high-level statistics, we posit that social networking and reciprocal sharing of fine-grained self-tracker data can provide valuable context for individuals in making sense of their data. We present the design of an online platform called Citizense Makers (CM), which facilitates group sharing, annotating and discussion of self-tracker data. In a field trial of CM, we explore design issues around willingness to share data reciprocally; the importance of familiarity between individuals; and understandings of common activities in contextualising one's own data

Visual object detection from lifelogs using visual non-lifelog data

Limited by the challenge of insufficient training data, research into lifelog analysis, especially visual lifelogging, has not progressed as fast as expected. To advance research on object detection on visual lifelogs, this thesis builds a deep learning model to enhance visual lifelogs by utilizing other sources of visual (non-lifelog) data which is more readily available. By theoretical analysis and empirical validation, the first step of the thesis identifies the close connection and relation between lifelog images and non-lifelog images. Following that, the second phase employs a domain-adversarial convolutional neural network to trans- fer knowledge from the domain of visual non-lifelog data to the domain of visual lifelogs. In the end, the third section of this work considers the task of visual object detection of lifelog, which could be easily extended to other related lifelog tasks. One intended outcome of the study, on a theoretical level of lifelog research, is to iden- tify the relationship between visual non-lifelog data and visual lifelog data from the perspective of computer vision. On a practical point of view, a second intended outcome of the research is to demonstrate how to apply domain adaptation to enhance learning on visual lifelogs by transferring knowledge from visual non-lifelogs. Specifically, the thesis utilizes variants of convolutional neural networks. Furthermore, a third intended outcome contributes to the release of the corresponding visual non-lifelog dataset which corresponds to an existing visual lifelog one. Finally, another output from this research is the suggestion that visual object detection from lifelogs could be seamlessly used in other tasks on visual lifelogging

Semantic interpretation of events in lifelogging

The topic of this thesis is lifelogging, the automatic, passive recording of a person’s daily activities and in particular, on performing a semantic analysis and enrichment of lifelogged data. Our work centers on visual lifelogged data, such as taken from wearable cameras. Such wearable cameras generate an archive of a person’s day taken from a first-person viewpoint but one of the problems with this is the sheer volume of information that can be generated. In order to make this potentially very large volume of information more manageable, our analysis of this data is based on segmenting each day’s lifelog data into discrete and non-overlapping events corresponding to activities in the wearer’s day. To manage lifelog data at an event level, we define a set of concepts using an ontology which is appropriate to the wearer, applying automatic detection of concepts to these events and then semantically enriching each of the detected lifelog events making them an index into the events. Once this enrichment is complete we can use the lifelog to support semantic search for everyday media management, as a memory aid, or as part of medical analysis on the activities of daily living (ADL), and so on. In the thesis, we address the problem of how to select the concepts to be used for indexing events and we propose a semantic, density- based algorithm to cope with concept selection issues for lifelogging. We then apply activity detection to classify everyday activities by employing the selected concepts as high-level semantic features. Finally, the activity is modeled by multi-context representations and enriched by Semantic Web technologies. The thesis includes an experimental evaluation using real data from users and shows the performance of our algorithms in capturing the semantics of everyday concepts and their efficacy in activity recognition and semantic enrichment

Lifelog access modelling using MemoryMesh

As of very recently, we have observed a convergence of technologies that have led to the emergence of lifelogging as a technology for personal data application. Lifelogging will become ubiquitous in the near future, not just for memory enhancement and health management, but also in various other domains. While there are many devices available for gathering massive lifelogging data, there are still challenges to modelling large volume of multi-modal lifelog data. In the thesis, we explore and address the problem of how to model lifelog in order to make personal lifelogs more accessible to users from the perspective of collection, organization and visualization. In order to subdivide our research targets, we designed and followed the following steps to solve the problem: 1. Lifelog activity recognition. We use multiple sensor data to analyse various daily life activities. Data ranges from accelerometer data collected by mobile phones to images captured by wearable cameras. We propose a semantic, density-based algorithm to cope with concept selection issues for lifelogging sensory data. 2. Visual discovery of lifelog images. Most of the lifelog information we takeeveryday is in a form of images, so images contain significant information about our lives. Here we conduct some experiments on visual content analysis of lifelog images, which includes both image contents and image meta data. 3. Linkage analysis of lifelogs. By exploring linkage analysis of lifelog data, we can connect all lifelog images using linkage models into a concept called the MemoryMesh. The thesis includes experimental evaluations using real-life data collected from multiple users and shows the performance of our algorithms in detecting semantics of daily-life concepts and their effectiveness in activity recognition and lifelog retrieval

Decoding learning: the proof, promise and potential of digital education

With hundreds of millions of pounds spent on digital technology for education every year – from interactive whiteboards to the rise of one–to–one tablet computers – every new technology seems to offer unlimited promise to learning. many sectors have benefitted immensely from harnessing innovative uses of technology. cloud computing, mobile communications and internet applications have changed the way manufacturing, finance, business services, the media and retailers operate. But key questions remain in education: has the range of technologies helped improve learners’ experiences and the standards they achieve? or is this investment just languishing as kit in the cupboard? and what more can decision makers, schools, teachers, parents and the technology industry do to ensure the full potential of innovative technology is exploited? There is no doubt that digital technologies have had a profound impact upon the management of learning. institutions can now recruit, register, monitor, and report on students with a new economy, efficiency, and (sometimes) creativity. yet, evidence of digital technologies producing real transformation in learning and teaching remains elusive. The education sector has invested heavily in digital technology; but this investment has not yet resulted in the radical improvements to learning experiences and educational attainment. in 2011, the Review of Education Capital found that maintained schools spent £487 million on icT equipment and services in 2009-2010. 1 since then, the education system has entered a state of flux with changes to the curriculum, shifts in funding, and increasing school autonomy. While ring-fenced funding for icT equipment and services has since ceased, a survey of 1,317 schools in July 2012 by the british educational suppliers association found they were assigning an increasing amount of their budget to technology. With greater freedom and enthusiasm towards technology in education, schools and teachers have become more discerning and are beginning to demand more evidence to justify their spending and strategies. This is both a challenge and an opportunity as it puts schools in greater charge of their spending and use of technolog

Hardware for recognition of human activities: a review of smart home and AAL related technologies

Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard

Tracking in the wild: exploring the everyday use of physical activity trackers

As the rates of chronical diseases, such as obesity, cardiovascular disease and diabetes continue to increase, the development of tools that support people in achieving healthier habits is becoming ever more important. Personal tracking systems, such as activity trackers, have emerged as a promising class of tools to support people in managing their everyday health. However, for this promise to be fulfilled, these systems need to be well designed, not only in terms of how they implement specific behavior change techniques, but also in how they integrate into people’s daily lives and address their daily needs. My dissertations provides evidence that accounting for people’s daily practices and needs can help to design activity tracking systems that help people get more value from their tracking practices. To understand how people derive value from their activity tracking practices, I have conducted two inquiries into people’s daily uses of activity tracking systems. In a fist attempt, I led a 10-month study of the adoption of Habito, our own activity tracking mobile app. Habito logged not only users’ physical activity, but also their interactions with the app. This data was used to acquire an estimate of the adoption rate of Habito, and understanding of how adoption is affected by users’ ‘readiness’, i.e., their attitude towards behavior change. In a follow-up study, I turned to the use of video methods and direct, in-situ observations of users’ interactions to understand what motivates people to engage with these tools in their everyday life, and how the surrounding environment shapes their use. These studies revealed some of the complexities of tracking, while extending some of the underlying ideas of behavior change. Among key results: (1) people’s use of activity trackers was found to be predominantly impulsive, where they simultaneously reflect, learn and change their behaviors as they collect data; (2) people’s use of trackers is deeply entangled with their daily routines and practices, and; (3) people use of trackers often is not in line with the traditional vision of these tools as mediators of change – trackers are also commonly used to simply learn about behaviors and engage in moments of self-discovery. Examining how to design activity tracking interfaces that best support people’s different needs , my dissertation further describes an inquiry into the design space of behavioral feedback interfaces. Through a iterative process of synthesis and analysis of research on activity tracking, I devise six design qualities for creating feedback that supports people in their interactions with physical activity data. Through the development and field deployment of four concepts in a field study, I show the potential of these displays for highlighting opportunities for action and learning.À medida que a prevalência de doenças crónicas como a obesidade, doenças cardiovasculares e diabetes continua a aumentar, o desenvolvimento de ferramentas que suportam pessoas a atingir mudanças de comportamento tem-se tornado essencial. Ferramentas de monitorização de comportamentos, tais como monitores de atividade física, têm surgido com a promessa de encorajar um dia a dia mais saudável. Contudo, para que essa promessa seja cumprida, torna-se essencial que estas ferramentas sejam bem concebidas, não só na forma como implementam determinadas estratégias de mudança de comportamento, mas também na forma como são integradas no dia-a-dia das pessoas. A minha dissertação demonstra a importância de considerar as necessidades e práticas diárias dos utilizadores destas ferramentas, de forma a ajudá-las a tirar melhor proveito da sua monitorização de atividade física. De modo a entender como é que os utilizadores destas ferramentas derivam valor das suas práticas de monitorização, a minha dissertação começa por explorar as práticas diárias associadas ao uso de monitores de atividade física. A minha dissertação contribui com duas investigações ao uso diário destas ferramentas. Primeiro, é apresentada uma investigação da adoção de Habito, uma aplicação para monitorização de atividade física. Habito não só registou as instâncias de atividade física dos seus utilizadores, mas também as suas interações com a própria aplicação. Estes dados foram utilizados para adquirir uma taxa de adopção de Habito e entender como é que essa adopção é afetada pela “prontidão” dos utilizadores, i.e., a sua atitude em relação à mudança de comportamento. Num segundo estudo, recorrendo a métodos de vídeo e observações diretas e in-situ da utilização de monitores de atividade física, explorei as motivações associadas ao uso diário destas ferramentas. Estes estudos expandiram algumas das ideias subjacentes ao uso das ferramentas para mudanças de comportamento. Entre resultados principais: (1) o uso de monitores de atividade física é predominantemente impulsivo, onde pessoas refletem, aprendem e alteram os seus comportamentos à medida que recolhem dados sobe estes mesmos comportamentos; (2) o uso de monitores de atividade física está profundamente interligado com as rotinas e práticas dos seus utilizadores, e; (3) o uso de monitores de atividade física nem sempre está ligado a mudanças de comportamento – estas ferramentas também são utilizadas para divertimento e aprendizagem. A minha dissertação contribui ainda com uma exploração do design de interfaces para a monitorização de atividade física. Através de um processo iterativo de síntese e análise de literatura, seis qualidades para a criação de interfaces são derivadas. Através de um estudo de campo, a minha dissertação demonstro o potencial dessas interfaces para ajudar pessoas a aprender e gerir a sua saúde diária