Life editing: Third-party perspectives on lifelog content

Lifelog collections digitally capture and preserve personal experiences and can be mined to reveal insights and understandings of individual significance. These rich data sources also offer opportunities for learning and discovery by motivated third parties. We employ a custom-designed storytelling application in constructing meaningful lifelog summaries from third-party perspectives. This storytelling initiative was implemented as a core component in a university media-editing course. We present promising results from a preliminary study conducted to evaluate the utility and potential of our approach in creatively interpreting a unique experiential dataset

Exploring the Potential of a Wearable Camera to Examine the Early Obesogenic Home Environment: Comparison of SenseCam Images to the Home Environment Interview.

BACKGROUND: The obesogenic home environment is usually examined via self-report, and objective measures are required. OBJECTIVE: This study explored whether the wearable camera SenseCam can be used to examine the early obesogenic home environment and whether it is useful for validation of self-report measures. METHODS: A total of 15 primary caregivers of young children (mean age of child 4 years) completed the Home Environment Interview (HEI). Around 12 days after the HEI, participants wore the SenseCam at home for 4 days. A semistructured interview assessed participants' experience of wearing the SenseCam. Intraclass correlation coefficients (ICCs), percent agreement, and kappa statistics were used as validity estimates for 54 home environment features. RESULTS: Wearing the SenseCam was generally acceptable to those who participated. The SenseCam captured all 54 HEI features but with varying detail; 36 features (67%) had satisfactory validity (ICC or kappa ≥0.40; percent agreement ≥80 where kappa could not be calculated). Validity was good or excellent (ICC or kappa ≥0.60) for fresh fruit and vegetable availability, fresh vegetable variety, display of food and drink (except sweet snacks), family meals, child eating lunch or dinner while watching TV, garden and play equipment, the number of TVs and DVD players, and media equipment in the child's bedroom. Validity was poor (ICC or kappa <0.40) for tinned and frozen vegetable availability and variety, and sweet snack availability. CONCLUSIONS: The SenseCam has the potential to objectively examine and validate multiple aspects of the obesogenic home environment. Further research should aim to replicate the findings in a larger, representative sample

Everyday concept detection in visual lifelogs: validation, relationships and trends

The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a user's day-to-day activities. It can capture up to 3,000 images per day, equating to almost 1 million images per year. It is used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearer's life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the novel domain of visual lifelogs. A concept detector models the correspondence between low-level visual features and high-level semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concept's presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were then evaluated on a subset of 95,907 images, to determine the precision for detection of each semantic concept. We conduct further analysis on the temporal consistency, co-occurance and trends within the detected concepts to more extensively investigate the robustness of the detectors within this novel domain. We additionally present future applications of concept detection within the domain of lifelogging

Socio-technical lifelogging: deriving design principles for a future proof digital past

Lifelogging is a technically inspired approach that attempts to address the problem of human forgetting by developing systems that ‘record everything’. Uptake of lifelogging systems has generally been disappointing, however. One reason for this lack of uptake is the absence of design principles for developing digital systems to support memory. Synthesising multiple studies, we identify and evaluate 4 new empirically motivated design principles for lifelogging: Selectivity, Embodiment, Synergy and Reminiscence. We first summarise 4 empirical studies that motivate the principles, then describe the evaluation of 4 novel systems built to embody these principles. The design principles were generative, leading to the development of new classes of lifelogging system, as well as providing strategic guidance about how those systems should be built. Evaluations suggest support for Selection and Embodiment principles, but more conceptual and technical work is needed to refine the Synergy and Reminiscence principles

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The everyday functioning of individuals with cognitive difficulties and their families : going beyond neuropsychological assessment

The portfolio has three parts:Part One is a systematic literature review, in which the theoretical, conceptual and empirical literature relating to the active involvement of family members in interventions for adults with memory impairment is reviewed.Part Two is an empirical paper, which explores how objective cognitive performance translates into self-reported cognitive skills and diabetes self-management in individual with Type 1 Diabetes.Part Three comprises the appendices