Quantifying athlete wellness: Investigating the predictive potential of subjective wellness reports through a player monitoring system

This study investigated the potential of self-reported wellness data from a player monitoring system and its predictive power of individual match performance among a female professional football player cohort. Using longitudinal data collected from the Pm Reporter Pro mobile application and corresponding individual performance scores (InStat Index), the study investigated if pre-match perceived wellness could predict individual match performance. The results show no significant evidence for a correlation between the two. This result may suggest that other factors might have a larger impact on performance, that the data quality captured by the current version of the player monitoring system is not sufficient, or that the impact of personally perceived wellness on performance is minimal. The limitations of bias in self-reported data and relatively small sample size might have affected the results. Despite these findings, the study provides valuable insights into the use of data-driven analytics with a concrete and widely used player monitoring system and suggests recommendations for future research

Analysing privacy in visual lifelogging

The visual lifelogging activity enables a user, the lifelogger, to passively capture images from a first-person perspective and ultimately create a visual diary encoding every possible aspect of her life with unprecedented details. In recent years, it has gained popularities among different groups of users. However, the possibility of ubiquitous presence of lifelogging devices specifically in private spheres has raised serious concerns with respect to personal privacy. In this article, we have presented a thorough discussion of privacy with respect to visual lifelogging. We have re-adjusted the existing definition of lifelogging to reflect different aspects of privacy and introduced a first-ever privacy threat model identifying several threats with respect to visual lifelogging. We have also shown how the existing privacy guidelines and approaches are inadequate to mitigate the identified threats. Finally, we have outlined a set of requirements and guidelines that can be used to mitigate the identified threats while designing and developing a privacy-preserving framework for visual lifelogging

Privacy-Preserving and Secure Pilot Self-Assessment

There is a strong culture for using safety risk management tools to monitor the different parts of the operation in the aviation industry. However, most of the monitoring that is being done is done on the technical conditions of the aircraft rather than on the pilot. While there is an expectation that the pilots self-assess their health regarding how fit the pilot is to operate an aircraft, all the tools given are checklist-based. Checklists are widely used in the aviation industry to ensure all tasks are done as they should for mechanics and pilots. However, the drawback of checklist-based systems is that they do not monitor anything over time. As a pilot has responsibility for many passengers on every flight, the consequences of mistakes can be considerable. By not monitoring the health over time, some of the crucial information when considering whether the pilot is fit to fly or not may be forgotten. Fatigue and stress are two essential topics for ensuring the focus is on operating the aircraft rather than either zoning out or being concerned about something else during flight. As the EU work hour regulations exempt everyone within the aviation industry, pilots can work at any time during the day. As the pilots can work at any given point during the day, they have to self-regulate whether they can work. If they do not track topics such as sleep and nutrition, they can be fatigued and lose focus on the work to be done. This thesis presents Gearggus, a self-assessment tool that can assist the pilot in assessing their health based on the information given by a questionnaire. The user answers questions based on how important the data is monitored over time. Based on the answers, there is calculated feedback on how ready the pilot is to operate an aircraft. The data is presented on a history page, so the user can see what the score is based on and how to adjust to gain a better score. Gearggus was evaluated with a qualitative interview with experienced aviation personnel and the Department of Aviation employees at the Unversity of Tromsø. Both parties acknowledge the issues Gearggus is trying to solve, but with modifications to the system. The required changes differ between the parties

Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry

United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation. This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews

Towards Consent-Based Lifelogging in Sport Analytic

Lifelogging is becoming widely deployed outside the scope of solipsistic self quantification. In elite sport, the ability to utilize these digital footprints of athletes for sport analytic has already become a game changer. This raises privacy concerns regarding both the individual lifelogger and the bystanders inadvertently captured by increasingly ubiquitous sensing devices. This paper describes a lifelogging model for consented use of personal data for sport analytic. The proposed model is a stepping stone towards understanding how privacy-preserving lifelogging frameworks and run-time systems can be constructed