About the project
Objective
The project aims to tackle several methodological and technical challenges encountered during the first phase of developing digital twins (DT) of the personalized human neuromusculoskeletal system: durable biomass sensor design, real-time modelling, and multiple sensor fusion. Overcoming the abovementioned bottlenecks will vastly improve the reliability and robustness of the DT framework. It can then be a clinical-friendly biofeedback neurorehabilitation platform based on a highly modularized and robust wearable sensor-fusion framework, including the innovative biomass-based ultrasound transparent electromyography electrodes.
Background
It is estimated that 15% of the world’s population lives with one or more disabling conditions. Impaired motor function is one of the major disabilities. Management of a complex disability currently largely relies on rehabilitation. In clinical practice, the supervision and the evaluation of a rehabilitation motion pattern remain a medical and engineering challenge due to the lack of biofeedback information about the effect of the rehabilitation motion on individual human biological tissues and structures. Digital twins (DT) is one of the most important concepts in digitalization, integrating all data, models, and other information that allows us to monitor the current states of a real system, e.g., a human musculoskeletal system in the current context. Among others, reliable and wearable sensor data fusion is critical in accomplishing the workflow of DT. The recent development of epidermal electronics offers a promising alternative. In particular, natural wood-derived nanocellulose shows promise in epidermal electronics for simultaneous dual signals collection due to biocompatibility, excellent mechanical properties, high water retention, and great potential for multi-functionalization.
Crossdisciplinary collaboration
This project brings expertise within biomechanical modelling, medical imaging, artificial intelligence and wood nanoscience, wood nanoengineering, and biomaterials design, involving researchers from the Department of Engineering Mechanics at KTH SCI and the Department of Fibre and Polymer Technology at KTH CBH.
About the project
Objective
We propose to use hybrid testing to innovate data-driven solutions for cardiac assistance. The project aims to enable data-driven evaluation of novel cardiac support devices to allow a rich and healthy life for patients with cardiovascular disease.
Background
We are currently witnessing an epidemic of heart failure with a rising incidence in the general population worldwide (2–7%) and a mean survival of only five years. The last decade has seen tremendous advances in device-based treatment options. However, progress has stalled, with only one device being currently approved for use in humans.
At the same time, novel hybrid mock circulation loops have been developed, allowing physical device interaction with a digital model of the human cardiovascular system. Here at KTH, we built Sweden’s first cardiovascular hybrid mock circulation. In this way, we can mimic unprecedented amounts of virtual and physical implantations of potential candidates of cardiac assistive technologies. The hope is that machine learning approaches can aid in identifying the ideal position and actuation profile of the cardiac assist device of the future.
Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Electrical Engineering and Computer Science and the School of Chemistry, Biotechnology, and Health.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event:
About the project
Objective
Digital humans and AI are becoming an integrated part of society. Modern chatbots such as GPT3 (Generative Pre-trained Transformer 3) have shown a remarkable capability of generating human-like responses and may even be prompted to act sarcastic, depressed or shy. To bring such systems into an embodied agent, such as a digital human, the agent’s body motion should reflect the same psychological inner state. This is, however, lacking in modern synthesis systems of non-verbal behaviours, which only generate generic motion based on a neutral speaker.
In this project, we propose to enhance virtual agents’ state of the art by giving them a psychological inner state that colours their nonverbal behaviour. We term these agents artificial actors – virtual digital humans that can take directions and produce expressive and convincing acting behaviour, much like a real actor takes instructions from a director. This means that it should be possible to instruct a character not only what to do or talk about but also how these actions should be performed (e.g. as a shy person with social phobia).
The project includes
- A) recording a large database of acted behaviours containing a range of psychological states
- B) developing probabilistic generative methods to synthesize gestures from such high-level traits and,
- C) developing a psychological, cognitive model guiding the synthesis.
We will specifically create a virtual agent simulating a therapy patient and evaluate its performance with therapists or therapists in training.
Background
To be announced
Crossdisciplinary collaboration
The researchers in the team represent the School of Electrical Engineering and Computer Science, KTH and the Psychology Department at Stockholm University.
About the project
Objective
This project aims to establish an AI-based online platform for automated, and robust personalization and positioning of HBMs, focusing on baby HBMs. By this we eliminate the need for users to tackle personalization and positioning which is often challenging and tedious, thus the platform could be an tranformative tool for driving innovations relating to HBMs.
Background
Finite element HBMs are digitalized representations of the human body and have emerged as significant tools for driving industrial innovation and clinical applications. These models often are a baseline and in a specified position, and before the use of the HBMs, personalization and positioning of HBMs are needed. Despite continuous active development, HBM positioning remains challenging and tedious.
Crossdisciplinary collaboration
This project brings expertise within biomechanical modeling and artificial intelligence involving researchers from KTH School of Electrical Engineering and Computer Science and Applied AI at the Department of Industrial Systems at Research Institutes of Sweden (RISE).
Former project name: Virtual Baby Plattform
About the project
Objective
This project aims to combine magnetic resonance elastography (MRE) and advanced diffusion magnetic resonance imaging (dMRI) to develop the next generation of MRE methods to improve the biomechanical characterization of brain tissue.
More specifically, A) we will propose computational models that consider the diffusion processes to estimate biomechanical properties of the brain at a sub-voxel level and B) we will use advanced dMRI data to predict biomechanical properties without the expensive equipment required by MRE.
We will apply these new methods to data we are acquiring from Parkinson’s disease (PD) and cancer patients and healthy subjects and test their potential to improve the diagnosis and treatment of patients and to characterize biomechanical changes during ageing.
Background
Neurological disorders affect millions of persons worldwide. While MRI is crucial in clinical diagnostics, it is largely limited to morphological features. Understanding how the mechanical properties of tissues change due to disease can give valuable information for improving their early detection and diagnosis. A promising tool for non-invasively estimating these mechanical properties through magnetic resonance imaging (MRI) is Magnetic Resonance Elastography (MRE). The current MRE methods generate limited mechanical information about the brain tissue at a relatively low image resolution. The proposed methods will provide a better mechanical characterization of tissues for improved diagnosis. Moreover, using additional MRI modalities, we can better understand the mechanism behind the changes in mechanical properties due to diseases. This can eventually lead to better treatments for patients. MRE for the brain is only available in a few sites worldwide. In Autumn 2022, the KTH-owned MRI scanner became the first one in Sweden capable of performing MRE examinations in the brain.
Crossdisciplinary collaboration
In this project, we are establishing a new cross-disciplinary and complementary collaboration:
- Lisa Prahl Wittberg, Prof. in Multiphase flows / Fluid mechanics at the Department of Engineering Mechanics at KTH, is an expert in developing computational models to understand better the underlying processes of complex fluids in the human body.
- Rodrigo Moreno, Assoc. Prof. in Biomedical Imaging at KTH is an expert in using advanced AI applied to medical images. His group is currently involved in all projects in Sweden that use MRE for the brain.
- Christian Gasser, Prof. in tissue mechanics at KTH Mechanical Engineering, provides his expertise in the constitutive modelling of tissues needed in the project.
- Christoffer Olsson, a postdoc recruited for the previous phase of the project, will remain one of the key persons for developing the methods in this project.
- The data of this project is being collected in collaboration with Assoc. Prof. Armita Golkar from Stockholm Univ (SU), and Prof. Per Svenningsson, Assoc. Prof. Grégoria Kalpouzos and Assoc. Prof. Anna Falck Delgado from Karolinska Institute (KI).
About the project
Objective
The main objective of this project is to improve the productivity of two packaging lines within the SweOps Steriles function, as measured by Overall Equipment Effectiveness (OEE) and competence in line staffing. We aim to achieve the objective by enabling next-best-action decision support for front-line operators.
Background
Modern pharmaceutical packaging lines are complex systems with multiple intricate physical and digital components. Operators gain domain expertise through extended exposure and interaction with the systems. They see, touch, and listen to the operating parts of the system. With time, they develop deep procedural knowledge and reach the level where they can predict when the physical systems need maintenance. How do they do this? This is the basic question of the project SMART: Smart Predictive Maintenance for the Pharmaceutical Industry, a collaboration between AstraZeneca and KTH.
Our approach deploys three pillars: 1) sensor networks in manufacturing, 2) machine learning predictive models, and 3) interactive immersive and contextual visualizations. We will observe and interview expert operators to acquire their procedural knowledge and focus on the sensing and machine learning tools to produce a rich sensor-based predictive model that we visualize peripherally in the plant and immersively to the operators. The project aims to enhance the operators’ abilities to perform predictive maintenance and expedite the transfer of these skills to novice operators via novel digital tools.
Partner Postdoc
Tianzhi Li
Main supervisor
Lihui Wang, Professor and Chair of Sustainable Manufacturing at KTH
Co-supervisors
Jan Kronqvist, Assistant Professor at KTH
Ming Xiao, Associate Professor, Division of ISE at KTH EECS
Mario Romero, Associate professor at KTH EECS School, Division of Computational Science and Technology
Watch the recorded presentation at the Digitalize in Stockholm 2023 event:
About the project
Objective
Our goal is to detect breeding places using drones. The detection of the breeding places will happen in two steps. First, the drones will identify areas that need closer investigation at around 300m heights. In the second step, drones visit the waypoints. When coming to a potential breeding place, the task of the drone is to identify if the water is indeed a potential breeding place and whether or not it contains mosquito larvae. The project is expected to investigate several potential solutions to this problem. Once a breeding place with mosquito larvae is detected, the public health authorities and building owners will be informed to ensure removal.
Background
Dengue and Zika are two arboviral viruses that affect a significant portion of the world population. In Sri Lanka alone, the number of dengue cases has been substantial in recent years, with more than 150000 cases and 440 dengue deaths reported in 2017. While there is no direct correlation between the income level of the people and the possibility of being infected by the dengue virus, the economic impact on the poor is much larger despite free healthcare being available in Sri Lanka. The principal vector species of Dengue and Zika viruses are the mosquitoes Aedes aegypti and Aedes albopictus. They breed in very slow-flowing or standing water pools. It is important to reduce and control such potential breeding grounds to contain the spread of these diseases.
Crossdisciplinary collaboration
The researchers in the team represent the Information Science and Engineering at KTH and the Connected Intelligence Unit at RISE Research Institute of Sweden. The project cooperates with Kasun De Zoysa, University of Colombo, Sri Lanka.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event: