Model-Driven Data Integration for Emergency Response : Doctoral Dissertation for the Degree Philosophiae Doctor (PhD) at the Faculty of Engineering and Science, Specialization in Information and Communication Technology

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Visualization of Information Flows and Exchanged Information: Evidence from an indoor fire game

-Understanding information flows is essential to improve coordination information systems. Aims of such systems are typically reducing information overload and improving situational awareness. Yet, there is a lack of intuitive and easily understandable tools that help to structure and visualize the ad hoc information flows that occur during search and rescue operations. In this paper, we present the concept of such an analysis, and present findings from an indoor serious fire game. For this game, we describe the interactions of Emergency Responders (ER), including individual information (over-)load, and descriptions of content of communications. This approach therefore provides an effective way to learn about active teams, information flows, exchanged information, and overload

Representing fire emergency response knowledge through a domain modelling approach

When any kind of emergency occurs, Emergency Responders (ERs) from different emergency organizations (such as police, fire, ambulance and municipality) have to act concurrently to solve the difficulties which are posed at the emergency site. Moreover, during the immediate response, getting the awareness of the situation is very crucial for ERs to lessen the emergency impacts such as loss of life and damage to the property. However, this can only be done when ERs get access to the information in timely manner and share the acquired information with one another during emergency response. Despite ERs share knowledge with one another they have to use same concepts to obtain the semantic understanding in order to perform actions for achieving goals. In addition, the success of the emergency response lies on the ERs’ coordination and their interoperability (information systems interoperability). Therefore, in this paper we provide a formal structure to the concepts that describes the building fire emergency management domain in order to provide a common semantic understanding for ERs. In our study, domain modelling approach has been used to represent the concepts formally. The presented results combine the knowledge from semi-structured interviews, document analysis, and through literature review. The developed domain model includes four aspects: i) characteristics of the event, ii) actors involved, iii) goals to achieve and iv) Building characteristics. Besides, the developed domain model serves as a foundational component to create an information system to unify, facilitate and expedite access to emergency related information for facilitating data exchange format and enable knowledge sharing among different emergency actors

Pattern recognition based prediction of the outcome of radiotherapy in cervical cancer treatment

Cervical Cancer is one the most common cancers amongst women. Ev- ery year almost 300 Norwegian women are diagnosed with cervical cancer. It is the 5th most deadly cancer type amongst women in the world. Esti- mates show that there are approximately 473,000 cases of cervical cancer in 2008 and 253,500 deaths per year. As we can see from the statistics, cer- vical cancer is a very severe and common type of cancer which costs many human lives every year. Therefore any progression in prognostication of this disease is very essential to treatment of its patients. Our task in this project was to analyze contrast enhanced MR imaging data from 78 patients. This data was recorded after a certain period of time after the patients received radiotherapy. The data was collected after a median time of 48 months for each patient. The outcome of the treatment and propagation of the contrast medium in to the blood vessels (in tumor region) was recorded. The main focus of this project was to model spatial patterns in the Cervix Cancer data set using hidden Markov models (HMM) in one of the machine learning techniques can be used to predict the outcome of radiotherapy treatment of the cervical cancer patients based on identi ed patterns with given data samples. To nd the unobserved (hidden) patterns, we have used hidden Markov models on the dataset to nd hidden patterns in the data. These models show the distribution of the outcome of the treatment, grouped by the similarities between properties of the contrast medium in the blood vessels. Our research shows that hidden Markov models are not feasible for this dataset. It was not possible to retrieve any information with high enough accuracy to be able to predict outcome of radiotherapy treatment

A Conceptual Framework Proposal for a Noise Modelling Service for Drones in U-Space Architecture

In recent years, there has been a rapid growth in the development and usage of flying drones due to their diverse capabilities worldwide. Public and private sectors will actively use drone technology in the logistics of goods and transporting passengers in the future. There are concerns regarding privacy and noise exposure in and around the rural and urban environment with the rapid expansion. Further, drone noise could affect human health. European Union has defined a service-orientated architecture to provide air traffic management for drones, called U-space. However, it lacks a noise modelling service (NMS). This paper proposes a conceptual framework for such a noise modelling service for drones with a use case scenario and verification method. The framework is conceptualized based on noise modelling from the aviation sector. The NMS can be used to model the noise to understand the accepted drone noise levels in different scenarios and take measures needed to reduce the noise impact on the community

A Conceptual Framework Proposal for a Noise Modelling Service for Drones in U-Space Architecture

In recent years, there has been a rapid growth in the development and usage of flying drones due to their diverse capabilities worldwide. Public and private sectors will actively use drone technology in the logistics of goods and transporting passengers in the future. There are concerns regarding privacy and noise exposure in and around the rural and urban environment with the rapid expansion. Further, drone noise could affect human health. European Union has defined a service-orientated architecture to provide air traffic management for drones, called U-space. However, it lacks a noise modelling service (NMS). This paper proposes a conceptual framework for such a noise modelling service for drones with a use case scenario and verification method. The framework is conceptualized based on noise modelling from the aviation sector. The NMS can be used to model the noise to understand the accepted drone noise levels in different scenarios and take measures needed to reduce the noise impact on the community

Deep Learning for Classifying Physical Activities from Accelerometer Data

Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment

Deep Learning for Classifying Physical Activities from Accelerometer Data

Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment