About the project
Objective
- O1. Co-creation of a transformative user-centered design framework that integrates the lived experiences, preferences, and embodied interactions of older adults, paving the way for socially inclusive and empathetic SSRE designs that redefine mobility support.
- O2. Integration of biomechanical sensing, modeling, and control systems that seamlessly blend human intention, somatic feedback, and emotional cues, enabling SSREs to adapt dynamically and intuitively to the user’s physical and emotional states.
- O3. Development of human-SSRE interaction techniques through cutting-edge sensory-actuator designs, utilizing soft robotics, adaptive rhythms, and somatic alignment to create an unprecedented sense of trust, safety, and user empowerment in mobility.
- O4. Assess real-world adoption of SSREs by conducting studies that capture the full spectrum of their impact—mobility, trust, dignity, and quality of life—while providing actionable insights to drive societal integration.
Background
Exoskeletons are increasingly recognized as a potential mobility solution for older adults, aiming to support independence and health in aging populations. Studies show promising results, such as improved walking speed, endurance, and alleviation of gait issues in neurological conditions (Tricomi et al., 2024; Lakmazaheri et al., 2024; Kim et al., 2024). However, most devices are not explicitly designed for older adults or real-world settings, often tested on young, able-bodied males, overlooking motor decline and the complexities of aging user interactions. Few studies explore older adults’ experiences with exoskeletons in daily life (Shore et al., 2018, 2020), and participation in design or long-term evaluations is rare (Young et al., 2022). Trust, comfort, and safety are critical factors for acceptability but remain underexplored in user-centered design (Peng et al., 2022).
Theoretical frameworks highlight that trust in autonomous systems stems from sensory and embodied interactions (Pink, 2021), while emerging approaches, such as soma design have the capacity to place sustained focus on the sensory, embodied and experiential aspects of interaction with technology (Höök 2018). Soma design approaches as applied to autonomous systems can enhance feelings of trust and safety in other domains, such as in semi-autonomous vehicles (Balaam et al., 2024). However, the mechatronic systems essential for replicating nuanced sensory interactions are underdeveloped.
Though advancements in materials, human-in-the-loop control, and sensor technology provide insights into user movement and needs (Küçüktabak et al., 2023), real-world applications often fail to achieve the real-time adaptability required for intuitive use. This represents a significant limitation in achieving devices that “feel right” and foster trust in users. In addition, the capability to measure and predict human motion and intention has not yet been fully translated into real-world applications. While simulations of optimal external assistance can estimate user responses to some degree, the complexity of human movement and interaction limits their accuracy. Thus, current systems lack synchronization, crucial for aligning user actions with device responses (Wilkenfeld et al., 2023). Addressing these gaps is key to fostering trust, comfort, and usability in exoskeleton design and achieving widespread adoption.
Cross-disciplinary collaboration
This project brings together a Professor in Biomechanics, alongside a Professor in Interaction Design and an Assistant Professor in Mechatronics, representing the KTH school of Engineering Science, the KTH School of Electrical Engineering and Computing Science and the KTH School of Industrial Engineering and Management. This cross disciplinary expertise will allow us to develop new exoskeleton interaction technologies will provide new ways for users and exoskeletons to cooperate, improving user experiences of safety, efficiency, and pleasure of moving with these assitive devices. By placing older adults at the centre of design and development processes towards exoskeleton we expect to redefine mobility for aging adults by combining physical functionality with psychological and emotional care.
About the project
Objective
This project will provide novel methodology to reconstruct the evolutionary history of cancer cells in their spatial context from widely used data. We will integrate single-cell and spatial transcriptomics data to reconstruct the evolutionary history of cancer cells and describe their spatial structure. These results will reveal how different cancer cell states arise and organize in space during tumor evolution, and how different states may be shaped by their interactions with the tumor microenvironment.
Background
Single-cell sequencing data has enabled highly detailed descriptions of intra-tumor heterogeneity in terms of the genotypes and phenotypes of cancer cells, as well as maps of the non-cancer cell types present within the tumor microenvironment. While standard single-cell sequencing techniques such as scRNA-seq provide detailed information on the cell states that make up a tumor, they do not capture the spatial distribution of the cells that it captures, which is lost in the process. In contrast, spatial transcriptomics technologies maintain the spatial structure of 2D tumor slices intact while still obtaining transcriptome-wide measurements of the cells therein. Integrating both data types may reveal novel therapeutic targets.
About the Digital Futures Postdoc Fellow
Pedro F. Ferreira holds a PhD in Computational Biology from ETH Zürich in Switzerland and a MSc in Electrical and Computer Engineering from IST in Portugal. He is interested in using single-cell sequencing data to reconstruct cell lineages and trajectories in order to identify key processes involved in tumor progression. To this end, Pedro has developed computational tools to characterize the populations of cells that constitute a tumor. These include learning the evolutionary history of cancer cells and identifying the gene expression patterns of malignant and normal cells. Pedro enjoys collaborating with biologists, bioinformaticians and machine learning experts in order to design powerful computational methods able to describe the heterogeneous populations of cells that constitute tumors.
Main supervisor
Jens Lagergren, KTH
Co-supervisor
Joakim Lundeberg, KTH.
About the project
Objective
The objective of the project is to identify and characterize clusters of patients and their dynamics over time such that the patients respond optimally to medical caregivers’ interventions and medications. In collaboration with Karolinska Institute and Region Stockholm, we will focus on dementia patients for personalized treatments and develop an advanced AI-based predictive analysis method to help medical caregivers for their decisions.
Background
It has been observed that patients suffering of a same disease can respond differently to the same medication. This can slow down medical treatments and even worsen the disease prognoses. How can we then make medical treatments personalized to improve the disease progression of patients over time?
Dementia patients have multiple follow-ups over time, generating longitudinal data. In a large patient pool, there can be several clusters, some representing patients who are more receptive and doing better with interventions and medications, and other clusters representing a more limited scope. Individual patients may also change clusters over time. Predictive analysis is required to make treatment decisions based on a patient’s personalized profile and the patient’s similarity across other patients over time. Modern sequence-based AI-methods are useful to make predictions on this type of data, and topological data analysis can give insights about characteristics and relations between patients by studying the shape of the data. These methods can help us find clusters of patients, characterize their disease progression and develop a decision care system for personalized treatments.
About the Digital Futures Postdoc Fellow
Belén García Pascual completed her PhD in biomathematics in October 2024 at the University of Bergen (Norway). She developed mathematical and computational models to explore questions in evolutionary and cell biology, with a focus on mitochondrial genes and evolutionary progression pathways of antimicrobial resistance. During the PhD, Belén did an industry internship at DNV in Oslo (Norway) researching how large language models can generate realistic synthetic data in healthcare. Before, she took her master in topology at the University of Bergen, and her bachelor in mathematics at Complutense University of Madrid (Spain).
Main supervisor
Martina Scolamiero
Co-supervisor
Saikat Chatterjee
About the project
Objective
The main objective is to develop a Content-Based Image Retrieval (CBIR) system using a large database of longitudinal brain Magnetic Resonance Imaging (MRI) of patients with dementia. By using artificial intelligence, the system will detect patterns and similarities in the longitudinal images, empowering healthcare professionals to predict treatment outcomes and deliver personalized care. Eventually, this tool aims to simplify decision making, improve patient care and make healthcare more efficient and cost-effective.
Background
As the global population ages, dementia diseases are becoming increasingly prevalent, currently affecting approximately 47 million individuals and imposing an economic burden of around $2.8 trillion. Innovative computer-aided diagnosis techniques, particularly CBIR, have been transformed by enhancing the retrieval of relevant images for patients with or at risk of dementia. Scientific evidence suggests that spatio-temporal patterns from longitudinal recordings can significantly improve outcomes in cross-sectional studies. However, progress in this field has been hindered by limited access to longitudinal databases, small dataset sizes, and ineffective analytical methods.
About the Digital Futures Postdoc Fellow
Félix received his PhD in Electronic Engineering from the Universitat Politècnica de València. His research has centered on applying artificial intelligence and signal processing techniques to biomedical data analysis. His thesis focused on developing a state-of-the-art preterm labor prediction system using Electrohysterography (EHG), successfully addressing challenges such as low incidence rates and limited data availability.
His work spanned the entire research process, from clinical data acquisition and signal preprocessing to the design of an automated decision-making system. Building on this foundation, he has expanded his proficiency in medical imaging modalities, and gained experience with modern deep learning architectures.
Main supervisor
Rodrigo Moreno, Associate Professor, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of Technology
Co-supervisor
Chunliang Wang, researcher with Docent title at School of Technology and Health (STH), KTH Royal Institute of Technology
About the project
Objective
The research aims to fill the gap in women’s health by designing and studying the use of digital health technologies for intimate care and the tacit interpersonal relationships associated with intimate care. At the personal level, the research aims to improve awareness of intimate care by co-designing and developing innovative interactions with the technology using innovative design methods such as Soma Design. At the interpersonal level, the research aims to study the shared and domestic use of intimate health technologies between partners, such as fertility tracking, and how better care structures can be developed outside the home, such as in the workplace. The research aims to understand the trust factors in using algorithmic services for intimate health at the system level.
Background
Despite making important progress in women’s health, severe gaps prevail in how women’s health is understood and represented. Social and cultural taboos associated with the female body have long affected education, treatment, and access to healthcare. Digital approaches to women’s health similarly have been limited in their focus such that they fail to respond to the broad, bodily, and taboo challenges that women’s health brings.
About the Digital Futures Postdoc Fellow
Deepika Yadav is a postdoctoral research fellow in Stockholm Technology & Interaction Research (STIR) group. Her research lies at the intersection of Human-Computer Interaction and global development with specific interests in working for underrepresented groups in resource-constrained settings, women’s health, and well-being. Her latest research studies sociological contexts of interpersonal relationships of care in the workplace setting for lactating mothers.
Main supervisor
Airi Lampinen, Associate Professor, Department of Computer and Systems Sciences (DSV), Stockholm University.
Co-supervisor
Madeline Balaam, Associate Professor, Division of Media Technology and Interaction Designs, KTH.
Watch the recorded presentation at Digitalize in Stockholm 2022 event.
About the project
Objective
This research project aims to design and develop an AI prototype that strengthens the collaborative work in the sighted guiding partnership. In sighted guiding, the guide bends and offers its arm to the person being guided. Their physical connection allows companions to accomplish navigation collaboratively.
This project will advance perspectives that stress how access and independence are achieved through interdependence, opening up new opportunities to design AI-AT that supports cooperation between people through/with AI, better responds to people’s capacities, and therefore empowers people with VI in social life. Results will be technically innovative because of the increasing adaptability of AI-based AT to contextual, situational and personal factors and the capabilities of people with VI. This differs from previous approaches focused on object recognition and discrete tasks where the end-to-end scenario is easily defined.
Background
Over 30 million people live with Visual Impairments (VI) in Europe. Often this medical condition interferes with the individual’s abilities to perform activities of everyday life since in a world with a predominance of visual content, information access can be hard, tiring and frustrating. Nowadays, people with VI still suffer from exclusion, such as marginalisation and powerlessness, in an increasingly digitalised society. People with VI are early adopters of Assistive Technology (AT), and AI-based AT (e.g., smartphone applications) plays an increasing part in their daily lives. In Human-Computer Interaction (HCI) research, increased attention has been given to independent navigation. Here, AI technologies aim to solve a functional task, where the user follows turn-by-turn instructions to successfully reach a destination and receive physical spatial information, such as the identification and proximity of obstacles and landmarks. A promising alternative approach builds on the interdependence framework that sees AT as a way to extend the relations between one another, focusing on how actors are made more or less able, relationally, through other actors and through AT.
About the Digital Futures Postdoc Fellow
Beatrice Vincenzi, University of London, is a postdoc in the Interaction Design research group at KTH Royal Institute of Technology. She is interested in inclusivity and designing AI assistive technology for/with people with disabilities. She is passionate about exploring design methods which make space for accessibility, interdependence, and AI.
Main supervisor
Marianela Ciolfi Felice, KTH.
Co-supervisor
Sanna Kuoppamäki, KTH.
About the project
Objective
With over 1 billion people over 60 worldwide, creating technology that supports the aged to live independently for longer by assisting them in everyday tasks became essential. While companion robots are aimed toward this need, current technology falls short in maintaining engagement over long-term interactions. Among the reasons is the inability to learn from users and adapt, known as lifelong learning, especially in open-domain dialogue that is not limited to any topic.
This project aims to develop a long‐term memory model for open‐domain dialogue such that a robot can learn and recall a person’s attributes, preferences, and shared history to provide personalized assistance in a variety of tasks, such as performing preferred activities, adaptive collaboration in chores, and providing reminders based on their schedule and needs.
About the Digital Futures Postdoc Fellow
Bahar Irfan is a Postdoctoral researcher at KTH Digital Futures. Her research focuses on creating personal robots that can continually learn and adapt to assist everyday life. Previously, she was a Research and Development Associate at Evinoks Service Equipment Industry and Commerce Inc., developing customizable software for industrial robots and smart buffets. Before that, she worked as an R&D Lab Associate at Disney Research Los Angeles on emotional language adaptation in multiparty interactions.
She has a diverse background in robotics, from personalization in long-term human-robot interaction during her PhD at the University of Plymouth and SoftBank Robotics Europe as a Marie Skłodowska-Curie Actions fellow to user-centred task planning for household robotics during her MSc in computer engineering, and building robots for BSc in mechanical engineering at Boğaziçi University.
Main supervisor
Gabriel Skantze, Professor in Speech Communication and Technology, KTH.
Co-supervisor
Sanna Kouppamäki, Assistant Professor, Division of Technology in Health Care, KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.