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The aim of this project is to study the feasibility of using wearable devices for anxiety detection using machine learning models. By creating a robust framework for continuous monitoring and early assessment, it has the potential to meaningfully impact the users wellbeing. - Artificial Intelligence and Signal and Image Processing, Biomedical Engineering, Computer Software, Electrical and Electronic Engineering, Information Systems, Medical and Health Sciences
- Master Thesis, Semester Project
| This project aims to systematically explore how "context" can be conceptualized and used to model outcomes in clinical research. This project focuses on analyzing the StudentLife dataset to explore how capturing contextual data can reduce variation in key outcome measures, such as the PHQ-9 (a measure of depression). The project aims to model how contextual covariates can explain variation over time, particularly during periods of increasing stress, such as a college semester. The findings will contribute to a viewpoint paper and a detailed analysis of the impact of contextual data on reducing trial sizes in research. - Health Information Systems (incl. Surveillance)
- Master Thesis
| The aim of this project is to study the feasibility of using wearable devices for early stage prediabetes detection using machine learning models. By creating a robust framework for continuous monitoring and risk assessment in at-risk populations, it has the potential to revolutionize how diabetes is detected and managed in clinical settings. - Artificial Intelligence and Signal and Image Processing, Biomedical Engineering, Computer Software, Medical and Health Sciences
- Master Thesis, Semester Project
| This project aims to systematically explore how "context" is conceptualized and represented within conceptual models across various Therapeutic Areas (TAs) in clinical research. The goal is to review existing literature, analyze qualitative research, and identify common themes and differences in how context, environment, and adaptation are integrated into these models. The project will result in a comprehensive review paper, the development of an initial ontology, and a gap analysis of existing datasets and sensors used to capture contextual information. - Health Information Systems (incl. Surveillance)
- ETH Zurich (ETHZ), Master Thesis
| The aim of this project is to predict neurological recovery after a cardiac arrest using machine learning methods and ECG and EEG data.
- Artificial Intelligence and Signal and Image Processing, Biomedical Engineering, Computer Software, Electrical and Electronic Engineering, Information Systems, Medical and Health Sciences
- Master Thesis, Semester Project
| The aim of this project is to implement a smartphone app that characterizes the light spectrum emitted by luminescent textile fibers. - Artificial Intelligence and Signal and Image Processing, Biomedical Engineering, Computer Software, Electrical and Electronic Engineering, Information Systems, Medical and Health Sciences
- Master Thesis, Semester Project
| Biological intelligence excels in adapting to new tasks by reusing prior knowledge, as seen in landmark-based navigation where animals plan routes using spatial relationships. In contrast, machine learning struggles with navigation due to challenges like understanding the effects of actions on perception (equivariance), estimating distances to objects without supervision (localization), and dealing with occlusion. The proposed method addresses these challenges by learning equivariant object representations and composing them into a coherent map, enabling accurate pose estimation and navigation. Successfully tested in a 3D simulation, the goal is to apply this approach to more realistic environments, such as drone navigation, and to demonstrate its utility in various tasks through reinforcement learning. - Artificial Intelligence and Signal and Image Processing
- Course Project
| Background Mental Workload:
Mental workload, also known as cognitive workload, refers to the mental effort and resources required to perform a specific task or activity. It encompasses various cognitive processes such as attention, memory, problem-solving, decision-making, and perception. The more demanding a task, the higher the mental workload required to perform it effectively. In the context of Human-Computer Interaction (HCI), mental workload is critical as it affects the user's ability to process information efficiently. When the workload exceeds a user's cognitive capacity, information processing slows down, leading to increased errors and reduced performance. Understanding and measuring mental workload is crucial because it affects our productivity, mental health, and overall well-being.
Background Stroke:
Stroke is a major cause of motor impairments, leading to an increased demand for effective rehabilitation methods. Technological solutions like VR-based rehabilitation and robotic movement training systems show promise in minimally supervised settings. However, maintaining patient engagement is a challenge. Balancing visual, memory, and attentional load is critical, especially for stroke patients sensitive to excessive task load. This project aims to develop an adaptive neurorehabilitation system using fNIRS to measure and adjust mental workload, enhancing patient engagement and recovery. - Behavioural and Cognitive Sciences, Engineering and Technology, Information, Computing and Communication Sciences, Medical and Health Sciences
- Bachelor Thesis, Collaboration, Internship, Lab Practice, Master Thesis, Semester Project
| This project investigates the relationship between corticomuscular coherence (CMC) and both muscular and cognitive fatigue during dual-task performance. Using synchronized EMG and EEG data collected via the Quattrocento system from OTBioelettronica, we aim to explore CMC variations at the onset of these signals during solo and dual motor-cognitive tasks. By analyzing data from motor (e.g., reach-and-grab) and cognitive (e.g., reading) activities, we will develop algorithms for automatic detection of EMG and EEG signal onsets. The goal is to validate whether CMC coherence increases during dual tasks, contributing to interventions for fatigue management and cognitive-motor training. This research, in collaboration with IIT Genoa and led by Dr. Marianna Semprini, seeks to enhance our understanding of cognitive-muscular interactions through CMC. - Engineering and Technology, Medical and Health Sciences
- Bachelor Thesis, Master Thesis
| To integrate robots into daily life, they must learn to manipulate diverse environments and objects. Recent advances in imitation learning show promise for teaching visual-motor skills, but require extensive robot-specific data. Reinforcement learning in simulation can learn robust policies in varied settings but struggles with the sim-to-real gap, especially with complex systems and camera observations. This work combines both approaches: using imitation learning to control a five-fingered hand from RGB images and reinforcement learning to control a quadruped's base and arm. - Intelligent Robotics, Knowledge Representation and Machine Learning, Robotics and Mechatronics
- Master Thesis
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