System (for) Tracking Equilibrium and Determining Incline (STEADI)

The goal of this project was to design and implement a smartphone-based wearable system to detect fall events in real time. It has the acronym STEADI. Rather than have expensive customised hardware STEADI was implemented in a cost effective manner using a generic mobile computing device. In order to detect the fall event, we propose a fall detector that uses the accelerometer available in a mobile phone. As for detecting a fall we mainly divide the system in two sections, the signal processing and classification. For the processing both a median filter and a high pass filter are used. A Median filter is used to amplify/enhance the signal by removing impulsive noise while preserving the signal shape while the High pass filter is used to emphasise transitions in the signal. Then, in order to recognize a fall event, our STEADI system implements two methods that are a simple threshold analysis to determine whether or not a fall has occurred (threshold-based) and a more sophisticated Naïve-Bayes classification method to differentiate falling from other mobile activities. Our experimental results show that by applying the signal processing and Naïve-Bayes classification together increases the accuracy by more than 20% compared with using the threshold-based method alone. The Naïve-Bayes achieved a detection accuracy of 95% in overall. Furthermore, an external sensor is introduced in order to enhance its accuracy. In addition to the fall detection, the systems can also provide location information using Google Maps as to the whereabouts of the fall event using the available GPS on the smartphone and sends the message to the caretaker via an SMS

System (for) Tracking Equilibrium and Determining Incline (STEADI)

The goal of this project was to design and implement a smartphone-based wearable system to detect fall events in real time. It has the acronym STEADI. Rather than have expensive customised hardware STEADI was implemented in a cost effective manner using a generic mobile computing device. In order to detect the fall event, we propose a fall detector that uses the accelerometer available in a mobile phone. As for detecting a fall we mainly divide the system in two sections, the signal processing and classification. For the processing both a median filter and a high pass filter are used. A Median filter is used to amplify/enhance the signal by removing impulsive noise while preserving the signal shape while the High pass filter is used to emphasise transitions in the signal. Then, in order to recognize a fall event, our STEADI system implements two methods that are a simple threshold analysis to determine whether or not a fall has occurred (threshold-based) and a more sophisticated Naïve-Bayes classification method to differentiate falling from other mobile activities. Our experimental results show that by applying the signal processing and Naïve-Bayes classification together increases the accuracy by more than 20% compared with using the threshold-based method alone. The Naïve-Bayes achieved a detection accuracy of 95% in overall. Furthermore, an external sensor is introduced in order to enhance its accuracy. In addition to the fall detection, the systems can also provide location information using Google Maps as to the whereabouts of the fall event using the available GPS on the smartphone and sends the message to the caretaker via an SMS

Conflict-free access rules for sharing smart patient health records

This research is funded by the EU H2020 project Serums (Securing Medical Data in Smart Patient-Centric Healthcare Systems), grant code 826278.With an increasing trend in personalised healthcare provision across Europe, we need solutions to enable the secure transnational sharing of medical records, establishing granular access rights to personal patient data. Access rules can establish what should be accessible by whom for how long, and comply with collective regulatory frameworks, such as the European General Data Protection Regulation (GDPR). The challenge is to design and implement such systems integrating novel technologies like Blockchain and Data Lake to enhance security and access control. The blockchain module must deal with adequate policies and algorithms to guarantee that no data leaks occur when authorising data retrieval requests. The data lake module tackles the need for an efficient way to retrieve potential granular data from heterogeneous data sources. In this paper, we define a patient-centric authorisation approach, incorporating a structured format for composing access rules that enable secure data retrieval and automatic rules conflict checking.Postprin

Epistolary poetry by Dominican Toma Marinković Tomić (1710-1779)

U članku se na temelju literature, objavljenih izvora i arhivske građe prikazuje život i rad hrvatskog pjesnika, dominikanca Tome Marinkovića Tomića (1710-1779), koji je praktički nepoznat u povijesti hrvatske književnosti. Autor članka govori o njegovoj poslanici u safičkim strofama na hrvatskome jeziku, opisujući povod njezina nastanka, formu i opseg te iznoseći njezin kratki sadržaj. Naglašava se da je poslanica posebno zanimljiva zbog jezika i terminologije. Potom se prvi put objavljuje prijepis te poslanice, pri čemu se u bilješkama iznosi nekoliko nužnih razjašnjenja, vezanih uz terminologiju redovničkoga života. Poziva se povjesničare književnosti da posvete više pozornosti poslanici o. Tome Marinkovića Tomića, što je bilo nemoguće prije njezina objavljivanja, kako bi na taj način dobila pravo mjesto u povijesti hrvatske književnosti.Based on references, published sources and archival materials, the paper attempts to show the life and work of a Croatian poet, Dominican Toma Marinković Tomić (1710-1779), who is virtually unknown in the history of Croatian literature. The author of the paper is discussing his epistle in Sapphic stanzas in Croatian language, describing the cause of its emergence, its form and size, also briefly giving its plot outline. It is pointed out that the epistle is particularly interesting because of its language and terminology. The transcription of the epistle is also published for the first time, providing in the notes several necessary explanations regarding the terminology from priestly life. Literary historians are invited to pay more attention to the epistle written by father Toma Marinković Tomić, so that in that way it would receive a proper place in the history of Croatian literature, which was impossible before its publication

A simulation-based approach for the behavioural analysis of cancer pathways

Cancer pathway is the name given to a patient’s journey from initial suspicion of cancer through to a confirmed diagnosis and, if applicable, the definition of a treatment plan. Typically, a cancer patient will undergo a series of procedures, which we designate as events, during their cancer care. The initial stage of the pathway, from suspected diagnosis to confirmed diagnosis and start of a treatment is called cancer waiting time(CWT). This paper focuses on the modelling and analysis of the CWT. Health boards are under pressure to ensure that the duration of CWT satisfies predefined targets. In this paper, we first create the visual representation of the pathway obtained from real patient data at a given health board, and then compare it with the standardised pathway considered by the board to find and flag a deviation in the execution of the cancer pathway. Next, we devise a discrete event simulation model for the cancer waiting time pathway. The input data is obtained from historical records of patients. The outcomes from this analysis highlight the pathway bottlenecks and transition times which maybe used to reveal potential improvements for CWT in the future.Postprin

Security and usability of a personalized user authentication paradigm : insights from a longitudinal study with three healthcare organizations

Funding information: This research has been partially supported by the EU Horizon 2020 Grant 826278 "Securing Medical Data in Smart Patient-Centric Healthcare Systems" (Serums) , and the Research and Innovation Foundation (Project DiversePass: COMPLEMENTARY/0916/0182).This paper proposes a user-adaptable and personalized authentication paradigm for healthcare organizations, which anticipates to seamlessly reflect patients’ episodic and autobiographical memories to graphical and textual passwords aiming to improve the security strength of user-selected passwords and provide a positive user experience. We report on a longitudinal study that spanned over three years in which three public European healthcare organizations participated in order to design and evaluate the aforementioned paradigm. Three studies were conducted (n=169) with different stakeholders: i) a verification study aiming to identify existing authentication practices of the three healthcare organizations with diverse stakeholders (n=9); ii) a patient-centric feasibility study during which users interacted with the proposed authentication system (n=68); and iii) a human guessing attack study focusing on vulnerabilities among people sharing common experiences within location-aware images used for graphical passwords (n=92). Results revealed that the suggested paradigm scored high with regards to users’ likeability, perceived security, usability and trust, but more importantly it assists the creation of more secure passwords. On the downside, the suggested paradigm introduces password guessing vulnerabilities by individuals sharing common experiences with the end-users. Findings are expected to scaffold the design of more patient-centric knowledge-based authentication mechanisms within nowadays dynamic computation realms.PostprintPeer reviewe

System (for) Tracking Equilibrium and Determining Incline (STEADI)

The goal of this project was to design and implement a smartphone-based wearable system to detect fall events in real time. It has the acronym STEADI. Rather than have expensive customised hardware STEADI was implemented in a cost effective manner using a generic mobile computing device. In order to detect the fall event, we propose a fall detector that uses the accelerometer available in a mobile phone. As for detecting a fall we mainly divide the system in two sections, the signal processing and classification. For the processing both a median filter and a high pass filter are used. A Median filter is used to amplify/enhance the signal by removing impulsive noise while preserving the signal shape while the High pass filter is used to emphasise transitions in the signal. Then, in order to recognize a fall event, our STEADI system implements two methods that are a simple threshold analysis to determine whether or not a fall has occurred (threshold-based) and a more sophisticated Naïve-Bayes classification method to differentiate falling from other mobile activities. Our experimental results show that by applying the signal processing and Naïve-Bayes classification together increases the accuracy by more than 20% compared with using the threshold-based method alone. The Naïve-Bayes achieved a detection accuracy of 95% in overall. Furthermore, an external sensor is introduced in order to enhance its accuracy. In addition to the fall detection, the systems can also provide location information using Google Maps as to the whereabouts of the fall event using the available GPS on the smartphone and sends the message to the caretaker via an SMS

System (for) Tracking Equilibrium and Determining Incline (STEADI)

The goal of this project was to design and implement a smartphone-based wearable system to detect fall events in real time. It has the acronym STEADI. Rather than have expensive customised hardware STEADI was implemented in a cost effective manner using a generic mobile computing device. In order to detect the fall event, we propose a fall detector that uses the accelerometer available in a mobile phone. As for detecting a fall we mainly divide the system in two sections, the signal processing and classification. For the processing both a median filter and a high pass filter are used. A Median filter is used to amplify/enhance the signal by removing impulsive noise while preserving the signal shape while the High pass filter is used to emphasise transitions in the signal. Then, in order to recognize a fall event, our STEADI system implements two methods that are a simple threshold analysis to determine whether or not a fall has occurred (threshold-based) and a more sophisticated Naïve-Bayes classification method to differentiate falling from other mobile activities. Our experimental results show that by applying the signal processing and Naïve-Bayes classification together increases the accuracy by more than 20% compared with using the threshold-based method alone. The Naïve-Bayes achieved a detection accuracy of 95% in overall. Furthermore, an external sensor is introduced in order to enhance its accuracy. In addition to the fall detection, the systems can also provide location information using Google Maps as to the whereabouts of the fall event using the available GPS on the smartphone and sends the message to the caretaker via an SMS

Facilitating the analysis and management of data for cancer care

The Edinburgh Cancer Centre (ECC) is an institution containing the National Health Service (NHS) Lothian cancer patient data from multiple resources. These resources are scattered across different systems and platforms, making it difficult to use the information collected in a useful way. There is a lack of proxy between the different (sub)systems, and this thesis presents a series of applications/projects to promote data usage and interoperability. We develop both front-end and back-end applications to bring together several databases, such as ChemoCare, Trak, and Oncology database. We create the South East Scotland Oncology (SESO) Gateway to improve the quality and capability of reporting outcomes within South East Scotland Oncology databases in real-time using routinely captured and integrated electronic healthcare data. With SESO Gateway, we focus on cancer pathway data visualisation for both the personal timeline and the cohort summary for various treatments. We also carry out a database migration and evaluate several reporting services for the newly migrated database to accelerate data access. We then perform data analysis for the patient's treatment waiting time. By analysing the waiting time and comparing it to the intended pathway, we can simplify the auditing process of the first stage of patients' cancer care journey. Further, we use the patients' treatment data, recorded toxicity level, and various observations concerning breast cancer patients to create models to analyse the outcome of the treatments, mainly chemotherapy. We compare several different techniques applied to the same data set to predict the toxicity outcome of the treatment. Through analysis and evaluation of the performance of these techniques, we can determine which method is more suitable in different situations to assist the oncologists in real-time clinical practice. After training the models, we create a dashboard as a placeholder for the models. Lastly, we explore how to define rules for cancer data and use a constraint based approach to fabricate a large cancer dataset, which will allow us to explore more techniques and further improve our system capability in the future. With our proposed systems, healthcare professionals can directly access and analyse patient data to gain further insights regarding the treatment that is best suited for an individual patient