On the integration of self-tracking data amongst quantified self members

Self-tracking, the process of recording one's own behaviours, thoughts and feelings, is a popular approach to enhance one's self-knowledge. While dedicated self-tracking apps and devices support data collection, previous research highlights that the integration of data constitutes a barrier for users. In this study we investigated how members of the Quantified Self movement---early adopters of self-tracking tools---overcome these barriers. We conducted a qualitative analysis of 51 videos of Quantified Self presentations to explore intentions for collecting data, methods for integrating and representing data, and how intentions and methods shaped reflection. The findings highlight two different intentions---striving for self-improvement and curiosity in personal data---which shaped how these users integrated data, i.e. the effort required. Furthermore, we identified three methods for representing data---binary, structured and abstract---which influenced reflection. Binary representations supported reflection-in-action, whereas structured and abstract representations supported iterative processes of data collection, integration and reflection. For people tracking out of curiosity, this iterative engagement with personal data often became an end in itself, rather than a means to achieve a goal. We discuss how these findings contribute to our current understanding of self-tracking amongst Quantified Self members and beyond, and we conclude with directions for future work to support self-trackers with their aspirations.

In bed with technology: Challenges and opportunities for sleep tracking

In recent years a variety of mobile apps, wearable technologies and embedded systems have emerged that allow individuals to track the amount and the quality of their sleep in their own beds. Despite the widespread adoption of these technologies, little is known about the challenges that current users face in tracking and analysing their sleep. Hence we conducted a qualitative study to examine the practices of current users of sleep tracking technologies and to identify challenges in current practice. Based on data collected from 5 online forums for users of sleep-tracking technologies, we identified 22 different challenges under the following 4 themes: tracking continuity, trust, data manipulation, and data interpretation. Based on these results, we propose 6 design opportunities to assist researchers and practitioners in designing sleep-tracking technologies.

The law and ethics of ‘self-quantified’ health information: An Australian perspective

This exploratory article examines the phenomenon of the ‘Quantified Self’—until recently, a subculture of enthusiasts who aim to discover knowledge about themselves and their bodies through self-tracking, usually using wearable devices to do so—and its implications for laws concerned with regulating and protecting health information. Quantified Self techniques and the ‘wearable devices’ and software that facilitate them—in which large transnational technology corporations are now involved—often involve the gathering of what would be considered ‘health information’ according to legal definitions, yet may occur outside the provision of traditional health services (including ‘e-health’) and the regulatory frameworks that govern them. This article explores the legal and regulatory framework for self-quantified health information and wearable devices in Australia and determines the extent to which this framework addresses privacy and other concerns that these techniques engender, along with suggestions for reform.

Exploring physical activities in an employer-sponsored health program

This work brings a perspective from an employer-sponsored health and wellness program called Global Corporate Challenge (GCC) to the 'quantified self' research. We present preliminary findings from a study with 17 university employees who participated in the GCC. We aimed to explore how participants derived meaningfulness from their self-tracking experiences. Our findings echo the growing body of work that advocates for conceptualizing activity tracking beyond the rationalistic, data-oriented perspectives and supporting more social and lived experiences.

Improved detection and tracking of objects in surveillance video

Surveillance networks are typically monitored by a few people, viewing several monitors displaying the camera feeds. It is then very difficult for a human operator to effectively detect events as they happen. Recently, computer vision research has begun to address ways to automatically process some of this data, to assist human operators. Object tracking, event recognition, crowd analysis and human identification at a distance are being pursued as a means to aid human operators and improve the security of areas such as transport hubs. The task of object tracking is key to the effective use of more advanced technologies. To recognize an event people and objects must be tracked. Tracking also enhances the performance of tasks such as crowd analysis or human identification. Before an object can be tracked, it must be detected. Motion segmentation techniques, widely employed in tracking systems, produce a binary image in which objects can be located. However, these techniques are prone to errors caused by shadows and lighting changes. Detection routines often fail, either due to erroneous motion caused by noise and lighting effects, or due to the detection routines being unable to split occluded regions into their component objects. Particle filters can be used as a self contained tracking system, and make it unnecessary for the task of detection to be carried out separately except for an initial (often manual) detection to initialise the filter. Particle filters use one or more extracted features to evaluate the likelihood of an object existing at a given point each frame. Such systems however do not easily allow for multiple objects to be tracked robustly, and do not explicitly maintain the identity of tracked objects. This dissertation investigates improvements to the performance of object tracking algorithms through improved motion segmentation and the use of a particle filter. A novel hybrid motion segmentation / optical flow algorithm, capable of simultaneously extracting multiple layers of foreground and optical flow in surveillance video frames is proposed. The algorithm is shown to perform well in the presence of adverse lighting conditions, and the optical flow is capable of extracting a moving object. The proposed algorithm is integrated within a tracking system and evaluated using the ETISEO (Evaluation du Traitement et de lInterpretation de Sequences vidEO - Evaluation for video understanding) database, and significant improvement in detection and tracking performance is demonstrated when compared to a baseline system. A Scalable Condensation Filter (SCF), a particle filter designed to work within an existing tracking system, is also developed. The creation and deletion of modes and maintenance of identity is handled by the underlying tracking system; and the tracking system is able to benefit from the improved performance in uncertain conditions arising from occlusion and noise provided by a particle filter. The system is evaluated using the ETISEO database. The dissertation then investigates fusion schemes for multi-spectral tracking systems. Four fusion schemes for combining a thermal and visual colour modality are evaluated using the OTCBVS (Object Tracking and Classification in and Beyond the Visible Spectrum) database. It is shown that a middle fusion scheme yields the best results and demonstrates a significant improvement in performance when compared to a system using either mode individually. Findings from the thesis contribute to improve the performance of semi-automated video processing and therefore improve security in areas under surveillance.

Self-identified experts lost on the interwebs

Security cues found in web browsers are meant to alert users to potential online threats, yet many studies demonstrate that security indicators are largely ineffective in this regard. Those studies have depended upon self-reporting of subjects' use or aggregate experimentation that correlate responses to sites with and without indicators. We report on a laboratory experiment using eye-tracking to follow the behavior of self-identified computer experts as they share information across popular social media websites. The use of eye-tracking equipment allows us to explore possible behavioral differences in the way experts perceive web browser security cues, as opposed to non-experts. Unfortunately, due to the use of self-identified experts, technological issues with the setup, and demographic anomalies, our results are inconclusive. We describe our initial experimental design, lessons learned in our experimentation, and provide a set of steps for others to follow in implementing experiments using unfamiliar technologies, eye-tracking specifically, subjects with different experience with the laboratory tasks, as well as individuals with varying security expertise. We also discuss recruitment and how our design will address the inherent uncertainties in recruitment, as opposed to design for an ideal population. Some of these modifications are generalizable, together they will allow us to run a larger 2x2 study, rather than a study of only experts using two different single sign-on systems.

Comparative eye tracking of experts and novices in web single sign-on

Security indicators in web browsers alert users to the presence of a secure connection between their computer and a web server; many studies have shown that such indicators are largely ignored by users in general. In other areas of computer security, research has shown that technical expertise can decrease user susceptibility to attacks. In this work, we examine whether computer or security expertise affects use of web browser security indicators. Our study takes place in the context of web-based single sign-on, in which a user can use credentials from a single identity provider to login to many relying websites; single sign-on is a more complex, and hence more difficult, security task for users. In our study, we used eye trackers and surveyed participants to examine the cues individuals use and those they report using, respectively. Our results show that users with security expertise are more likely to self-report looking at security indicators, and eye-tracking data shows they have longer gaze duration at security indicators than those without security expertise. However, computer expertise alone is not correlated with recorded use of security indicators. In survey questions, neither experts nor novices demonstrate a good understanding of the security consequences of web-based single sign-on.

Robust vision-based underwater homing using self-similar landmarks

Next-generation autonomous underwater vehicles (AUVs) will be required to robustly identify underwater targets for tasks such as inspection, localization, and docking. Given their often unstructured operating environments, vision offers enormous potential in underwater navigation over more traditional methods; however, reliable target segmentation often plagues these systems. This paper addresses robust vision-based target recognition by presenting a novel scale and rotationally invariant target design and recognition routine based on self-similar landmarks that enables robust target pose estimation with respect to a single camera. These algorithms are applied to an AUV with controllers developed for vision-based docking with the target. Experimental results show that the system performs exceptionally on limited processing power and demonstrates how the combined vision and controller system enables robust target identification and docking in a variety of operating conditions.

Towards robotic visual and acoustic stealth for outdoor dynamic target tracking

Covertly tracking mobile targets, either animal or human, in previously unmapped outdoor natural environments using off-road robotic platforms requires both visual and acoustic stealth. Whilst the use of robots for stealthy surveillance is not new, the majority only consider navigation for visual covertness. However, most fielded robotic systems have a non-negligible acoustic footprint arising from the onboard sensors, motors, computers and cooling systems, and also from the wheels interacting with the terrain during motion. This time-varying acoustic signature can jeopardise any visual covertness and needs to be addressed in any stealthy navigation strategy. In previous work, we addressed the initial concepts for acoustically masking a tracking robot’s movements as it travels between observation locations selected to minimise its detectability by a dynamic natural target and ensuring con- tinuous visual tracking of the target. This work extends the overall concept by examining the utility of real-time acoustic signature self-assessment and exploiting shadows as hiding locations for use in a combined visual and acoustic stealth framework.

Online self-supervised multi-instance segmentation of dynamic objects

This paper presents a method for the continuous segmentation of dynamic objects using only a vehicle mounted monocular camera without any prior knowledge of the object’s appearance. Prior work in online static/dynamic segmentation is extended to identify multiple instances of dynamic objects by introducing an unsupervised motion clustering step. These clusters are then used to update a multi-class classifier within a self-supervised framework. In contrast to many tracking-by-detection based methods, our system is able to detect dynamic objects without any prior knowledge of their visual appearance shape or location. Furthermore, the classifier is used to propagate labels of the same object in previous frames, which facilitates the continuous tracking of individual objects based on motion. The proposed system is evaluated using recall and false alarm metrics in addition to a new multi-instance labelled dataset to evaluate the performance of segmenting multiple instances of objects.

Active aging: Exploration into self-ratings of "being active," out-of-home physical activity, and participation among older Australian adults living in four different settings

We examined whether self-ratings of “being active” among older people living in four different settings (major city high and lower density suburbs, a regional city, and a rural area) were associated with out-of-home participation and outdoor physical activity. A mixed-methods approach (survey, travel diary, and GPS tracking over a one-week period) was used to gather data from 48 individuals aged over 55 years. Self-ratings of “being active” were found to be positively correlated with the number of days older people spent time away from home but unrelated to time traveled by active means (walking and biking). No significant differences in active travel were found between the four study locations, despite differences in their respective built environments.The findings suggest that additional strategies to the creation of “age-friendly” environments are needed if older people are to increase their levels of outdoor physical activity. “Active aging” promotion campaigns may need to explicitly identify the benefits of walking outdoors to ambulatory older people as a means of maintaining their overall health, functional ability, and participation within society in the long-term and also encourage the development of community-based programs in order to facilitate regular walking for this group.

Things you don't want to know about yourself: Ambivalence about tracking and sharing personal information for behaviour change

Technologies that facilitate the collection and sharing of personal information can feed people's desire for enhanced self-knowledge and help them to change their behaviour, yet for various reasons people can also be reluctant to use such technologies. This paper explores this tension through an interview study in the context of smoking cessation. Our findings show that smokers and recent ex-smokers were ambivalent about their behaviour change as well as about collecting personal information through technology and sharing it with other users. We close with a summary of three challenges emerging from such ambivalence and directions to address them.

Toward Self-Organising Service Communities

This paper discusses a framework in which catalog service communities are built, linked for interaction, and constantly monitored and adapted over time. A catalog service community (represented as a peer node in a peer-to-peer network) in our system can be viewed as domain specific data integration mediators representing the domain knowledge and the registry information. The query routing among communities is performed to identify a set of data sources that are relevant to answering a given query. The system monitors the interactions between the communities to discover patterns that may lead to restructuring of the network (e.g., irrelevant peers removed, new relationships created, etc.).