Department of Holistic Systems
Today, everyone is talking about big-data analytics, video streaming, machine learning, web search, mobile apps, etc., and we all take it for granted that these "services" work perfectly. However, underneath such applications there are large systems, and individual components of these systems are often “only” a part of a big ecosystem of integrated building blocks needed to enable a functional service. In this respect, the HOST department addresses challenges potentially covering all components of the entire system, from data creation to visualisation of the results.
Using a combination of basic and applied research, we are targeting numerous applications, many in the field of sports and medicine, where we have a holistic view of the system and perform basic research, do experimental prototyping and run experiments in the intended environments.
Currently, machine learning is an important component of our research where we for example, in real- time, aim to analyse athlete performance and detect diseases in medical videos. In our holistic systems view, not only the accuracy of the machine learning analysis is of importance, but also the complete pipe-line integration and the system performance (e.g., resource consumption and scalability). We architect complete systems and optimise for particular application requirements, both functional and non-functional, in order to provide a best possible quality of the service and a lowest possible resource consumption. Finally, we have a goal to put the results into use for the society where we contribute to open source projects and have spun of new industries
People at Department of Holistic Systems
Who we are?
Simula Metropolitan employees are researchers, postdoctoral fellows, PhD students, engineers and administrative people. We are from all over the world, ranging from newly educated to experienced researchers, all working on making research in digital engineering at the highest international level possible.
Projects at Department of Holistic Systems
Interview training of child-welfare and law-enforcement professionals interviewing maltreated children supported via artificial avatars
The interdisciplinary FRIPRO project aims to improve interviews with maltreated children through a training program using realistic and interactive child avatars.
The department of Holistic Systems (HOST) at SimulaMet will be working with the Faculty of Social Sciences at OsloMet. The project will begin on the 1st of April 2021 and end on the 31st of March in 2024. It is funded by The Research Council of Norway with 12 million NOK and will include three Ph.D. positions.
Maltreatment and abuse of children is a significant societal problem that has serious and damaging effects on children’s behavior, psychological development, and adjustment. Detection and prevention of violence and sexual abuse against children is, therefore, a high priority for Child Protective Services (CPS) and law-enforcement professionals. The conversations and investigative interviews that are conducted with these children must be of high quality. However, both Norwegian and international research shows that despite investments in methodology, the current interview and conversation skills still need to be improved.
By using an empirically informed training system in highly realistic child avatars, this project aims to develop and maintain the advanced skills needed for interviewing maltreated children. They will use data from past investigative interviews with maltreated children and create a real-looking avatar that is capable of expressing emotion and spontaneous responses.
The planned avatar will be a combination of technologies from multiple areas in computer science including AI, computer vision, and natural language processing. The aim is for the child avatars to be a part of an interview-training program that will be implemented in cooperation with the CPS and the police. The training system will be evaluated by the project scientists to judge effectiveness in relation to real-world needs.
The project also involves collaborations with researchers from Griffith University in Australia and the University of Cambridge in the United Kingdom.
FFC: Female Football Centre
The Department of Holistic Systems at SimulaMet is collaborating with UiT - The Arctic University of Norway and Forzasys AS on the Female Football Centre (FFC), funded by the Tromsø Research Foundation.
The main goal of the centre is to gain new and fundamental insights into what affects the performance and overall health of female elite football players. A general objective is to devise novel methodologies for epidemiological research that might impact research fields in both sports and medicine. In particular, we aim to develop a non-invasive, privacy-preserving technology that enables us to continuously quantify and monitor athlete behavior where we derive analytic insights from different perspectives (e.g., biomechanics, sports-specific science, medicine, coaches, and athletes).
In the current gold standards for epidemiology, observational prospective cohort studies include that cohort subjects are followed in detail over a longer period. This is an error-prone and tedious task that has for a long time been carried out using pen and paper, and later doing a manual, tedious analysis. Making this entire process easier is the main responsibility of the researchers from the Department of Holistic Systems at SimulaMet.
In cooperation with UiT and Forzasys AS, SimulaMet has earlier developed and used an automatic performance monitoring system for athletes used by both national and elite series soccer teams. The goal is to quantify and develop accurate analysis technologies that enable a personalized assessment and performance development of elite athletes.
The automatic performance monitoring system collects athletes’ subjective parameters, like training load, wellness, injury, and illness, using a small questionnaire-app running on their mobile phones, and the data is transferred to a cloud-based backend system. Then, from a trainer-portal, the data can be automatically visualized for both individual players and team overviews.
In FFC, the objective is to extend the system further to include female-specific parameters and introduce more automatic analysis using, for example, machine learning. We will host and develop the system for all the teams participating in the project, and we will initiate automatic analyses that might be able to predict future overuse injuries or to help maybe to find the best development process of a player or a team.
Svein Arne Pettersen (head of research at the School of sports sciences, UiT) is the centre leader, and he has collaborated with the researchers at SimulaMet for a long time.
Read more about the new Female Football Centre (FFC)
FFC is funded by Tromsø Research Foundation.
Publications at Department of Holistic Systems
Book Chapter
Smittestopp Backend
In Smittestopp − A Case Study on Digital Contact Tracing, 29-62. Vol. 11. Cham: Springer International Publishing, 2022.Status: Published
Smittestopp Backend
An efficient backend solution is of great importance for any large-scale system, and Smittestopp is no exception. The Smittestopp backend comprises various components for user and device registration, mobile app data ingestion, database and cloud operations, and web interface support. This chapter describes our journey from a vague idea to a deployed system. We provide an overview of the system internals and design iterations and discuss the challenges that we faced during the development process, along with the lessons learned. The Smittestopp backend handled around 1.5 million registered devices and provided various insights and analyses before being discontinued a few months after its launch.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Book Chapter |
| Year of Publication | 2022 |
| Book Title | Smittestopp − A Case Study on Digital Contact Tracing |
| Volume | 11 |
| Pagination | 29 - 62 |
| Date Published | 06/2022 |
| Publisher | Springer International Publishing |
| Place Published | Cham |
| ISBN Number | 978-3-031-05465-5 |
| ISBN | 2512-1677 |
| URL | https://link.springer.com/content/pdf/10.1007/978-3-031-05466-2.pdf |
| DOI | 10.1007/978-3-031-05466-2_3 |
Smittestopp analytics: Analysis of position data
In Smittestopp − A Case Study on Digital Contact Tracing, 63-79. Vol. 11. Cham: Springer International Publishing, 2022.Status: Published
Smittestopp analytics: Analysis of position data
Contact tracing applications generally rely on Bluetooth data. This type of data works well to determine whether a contact occurred (smartphones were close to each other) but cannot offer the contextual information GPS data can offer. Did the contact happen on a bus? In a building? And of which type? Are some places recurrent contact locations? By answering such questions, GPS data can help develop more accurate and better-informed contact tracing applications. This chapter describes the ideas and approaches implemented for GPS data within the Smittestopp contact tracing application.We will present the pipeline used and the contribution of GPS data for contextual information, using inferred transport modes and surrounding POIs, showcasing the opportunities in the use of GPS information. Finally,we discuss ethical and privacy considerations, as well as some lessons learned.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Book Chapter |
| Year of Publication | 2022 |
| Book Title | Smittestopp − A Case Study on Digital Contact Tracing |
| Volume | 11 |
| Pagination | 63 - 79 |
| Date Published | 06/2022 |
| Publisher | Springer International Publishing |
| Place Published | Cham |
| ISBN Number | 978-3-031-05465-5 |
| ISBN | 2512-1677 |
| URL | https://link.springer.com/content/pdf/10.1007/978-3-031-05466-2_4 |
| DOI | 10.1007/978-3-031-05466-2_4 |
Journal Article
Predicting an unstable tear film through artificial intelligence
Scientific Reports 12, no. 1 (2022): 21416.Status: Published
Predicting an unstable tear film through artificial intelligence
Dry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. In this pilot study, we retrospectively included clinical data from 431 patients with dry eye disease examined in the Norwegian Dry Eye Clinic to evaluate how artificial intelligence algorithms perform on clinical data related to dry eye disease. The data was processed and subjected to numerous machine learning classification algorithms with the aim to predict decreased tear film break-up time. Moreover, feature selection techniques (information gain and information gain ratio) were applied to determine which clinical factors contribute most to an unstable tear film. The applied machine learning algorithms outperformed baseline classifications performed with ZeroR according to included evaluation metrics. Clinical features such as ocular surface staining, meibomian gland expressibility and dropout, blink frequency, osmolarity, meibum quality and symptom score were recognized as important predictors for tear film instability. We identify and discuss potential limitations and pitfalls.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Scientific Reports |
| Volume | 12 |
| Issue | 1 |
| Pagination | 21416 |
| Date Published | 12/2022 |
| Publisher | Springer Nature |
| URL | https://www.nature.com/articles/s41598-022-25821-y |
| DOI | 10.1038/s41598-022-25821-y |
Automating tracking of cell division for human embryo development in time lapse videos
Human Reproduction 37, no. Supplement_1 (2022).Status: Published
Automating tracking of cell division for human embryo development in time lapse videos
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Human Reproduction |
| Volume | 37 |
| Issue | Supplement_1 |
| Date Published | Jun-06-2024 |
| Publisher | Oxford University Press |
| ISSN | 0268-1161 |
| URL | https://academic.oup.com/humrep/article-pdf/37/Supplement_1/deac107.233/... |
| DOI | 10.1093/humrep/deac107.233 |
Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation
Scientific Reports 12, no. 1 (2022): 19825.Status: Published
Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Scientific Reports |
| Volume | 12 |
| Issue | 1 |
| Pagination | 19825 |
| Date Published | Jan-12-2022 |
| Publisher | Nature |
| URL | https://www.nature.com/articles/s41598-022-24118-4https://www.nature.com... |
| DOI | 10.1038/s41598-022-24118-4 |
Synthesizing a Talking Child Avatar to Train Interviewers Working with Maltreated Children
Big Data and Cognitive Computing 6, no. 2 (2022): 62.Status: Published
Synthesizing a Talking Child Avatar to Train Interviewers Working with Maltreated Children
| Afilliation | Communication Systems, Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Big Data and Cognitive Computing |
| Volume | 6 |
| Issue | 2 |
| Pagination | 62 |
| Date Published | Jan-06-2022 |
| Publisher | MDPI |
| URL | https://www.mdpi.com/2504-2289/6/2/62https://www.mdpi.com/2504-2289/6/2/... |
| DOI | 10.3390/bdcc6020062 |
Towards the Neuroevolution of Low-level artificial general intelligence
Frontiers in Robotics and AI 9 (2022).Status: Published
Towards the Neuroevolution of Low-level artificial general intelligence
In this work, we argue that the search for Artificial General Intelligence should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding environment, which could change over time and exert pressure on the organism to allow for learning of new behaviors or environment models. Our hypothesis is that learning occurs through interpreting sensory feedback when an agent acts in an environment. For that to happen, a body and a reactive environment are needed. We evaluate a method to evolve a biologically- inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence, a framework for low-level artificial general intelligence. This method allows the evolutionary complexification of a randomly-initialized spiking neural network with adaptive synapses, which controls agents instantiated in mutable environments. Such a configuration allows us to benchmark the adaptivity and generality of the controllers. The chosen tasks in the mutable environments are food foraging, emulation of logic gates, and cart-pole balancing. The three tasks are successfully solved with rather small network topologies and therefore it opens up the possibility of experimenting with more complex tasks and scenarios where curriculum learning is beneficial.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Frontiers in Robotics and AI |
| Volume | 9 |
| Date Published | 10/2022 |
| Publisher | Frontiers |
| URL | https://www.frontiersin.org/articles/10.3389/frobt.2022.1007547/full |
| DOI | 10.3389/frobt.2022.1007547 |
Áika: A Distributed Edge System for AI Inference
Big Data and Cognitive Computing 6, no. 2 (2022): 68.Status: Published
Áika: A Distributed Edge System for AI Inference
Video monitoring and surveillance of commercial fisheries in world oceans has been proposed by the governing bodies of several nations as a response to crimes such as overfishing. Traditional video monitoring systems may not be suitable due to limitations in the offshore fishing environment, including low bandwidth, unstable satellite network connections and issues of preserving the privacy of crew members. In this paper, we present Áika, a robust system for executing distributed Artificial Intelligence (AI) applications on the edge. Áika provides engineers and researchers with several building blocks in the form of Agents, which enable the expression of computation pipelines and distributed applications with robustness and privacy guarantees. Agents are continuously monitored by dedicated monitoring nodes, and provide applications with a distributed checkpointing and replication scheme. Áika is designed for monitoring and surveillance in privacy-sensitive and unstable offshore environments, where flexible access policies at the storage level can provide privacy guarantees for data transfer and access.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | Big Data and Cognitive Computing |
| Volume | 6 |
| Issue | 2 |
| Pagination | 68 |
| Date Published | Jan-06-2022 |
| Publisher | MDPI |
| URL | https://www.mdpi.com/2504-2289/6/2/68 |
| DOI | 10.3390/bdcc6020068 |
Exploration of Different Time Series Models for Soccer Athlete Performance Prediction
MDPI Engineering Proceedings 18, no. 1 (2022): 37.Status: Published
Exploration of Different Time Series Models for Soccer Athlete Performance Prediction
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | MDPI Engineering Proceedings |
| Volume | 18 |
| Issue | 1 |
| Pagination | 37 |
| Publisher | MDPI |
| DOI | 10.3390/engproc2022018037 |
To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems
PLOS Digital Health 1, no. 2 (2022): e0000016.Status: Published
To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Journal | PLOS Digital Health |
| Volume | 1 |
| Issue | 2 |
| Pagination | e0000016 |
| Date Published | May-02-2023 |
| Publisher | PLOS |
| URL | https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.... |
| DOI | 10.1371/journal.pdig.000001610.1371/ |
