ETH Competence Center - Competence Center for Rehabilitation Engineering and Science (RESC)Acronym | RESC | Homepage | https://resc.ethz.ch/ | Country | Switzerland | ZIP, City | | Address | | Phone | | Type | Academy | Parent organization | ETH Zurich | Current organization | ETH Competence Center - Competence Center for Rehabilitation Engineering and Science (RESC) | Child organizations | | Members | |
Open OpportunitiesDespite the growing amount of work on applying causal discovery method with expert knowledge to areas of interest, few of them inspect the uncertainty of expert knowledge (what if the expert goes wrong?). This is highly important since that in scientific fields, causal discovery with expert knowledge should be cautious and an approach taking expert uncertainty into account will be more robust to potential bias induced by individuals. Therefore, we aim to develop an iterative causal discovery method with experts in the loop to enable continual interaction and calibration between experts and data.
Based on the qualifications of the candidates, we can arrange a subsidy/allowance for covering traveling or living costs. - Expert Systems, Health Information Systems (incl. Surveillance), Statistics
- Internship, Master Thesis, Semester Project
| This project focuses on developing an explainable Artificial Intelligence (xAI) framework based on graphical modeling (GM), to enhance the capacity and capability of medical AI. Collaborating with the Swiss Paraplegic Centre (SPZ) for validation, our goal is to improve the long-term prognosis of spinal cord injury (SCI) individuals. Through medical records and a multimodal sensory monitoring system, we aim to create digital twins capable of integrating diverse data sources, guiding medical treatment, and addressing common secondary health conditions in the SCI population. The envisioned GM-based digital twin (GMDT) will represent hierarchical relations across demographic features, functional abilities, daily activities, and health conditions for SCI individuals, allowing for downstream tasks such as prediction, causal inference, and counterfactual reasoning. The assimilation and evolution between the physical and digital twins will be implemented under the GM framework, promising advancements in personalized healthcare strategies and improved outcomes for SCI people. Please refer to the attached document for more details about the task description. Based on the candidate's qualifications, funding/allowance can be provided. - Biomedical Engineering, Digital Systems, Knowledge Representation and Machine Learning, Pattern Recognition, Simulation and Modelling
- ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| The process of evaluating sleep examinations and diagnosing sleep disorders through polysomnographies (PSGs) is labor-intensive as it requires manual analysis from sleep technicians and doctors. In collaboration with Clinic Barmelweid, a leading sleep and rehabilitation clinic in northwestern Switzerland, we plan to automate this process using machine learning models. Clinic Barmelweid conducts approximately 400-450 PSGs annually and has access to a dataset of more than 5,000 recordings. - Artificial Intelligence and Signal and Image Processing, Biomedical Engineering, Medical and Health Sciences
- Collaboration, ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| Pulmonary hypertension (PH) in newborns poses significant diagnostic challenges due to its association with various diseases and its impact on morbidity and mortality. Early and accurate detection is essential for effective management, yet current manual echocardiographic assessment is time-consuming and requires expertise. This project aims to develop an automated machine learning method using multimodal variational autoencoders (VAEs) and diffusion models to predict PH in newborns from ultrasound, ECG data, and clinical variables. Leveraging a cohort of 270 newborns from the University Children’s Hospital Regensburg, the project will enhance interpretability and feature representation by assessing the significance of each data type and utilizing synthetic data augmentation. The hybrid approach of combining VAEs with diffusion models is expected to improve prediction accuracy and generalization, advancing early detection and understanding of PH in newborns. - Biomedical Engineering, Computer Communications Networks, Electrical and Electronic Engineering, Image Processing, Signal Processing
- ETH Zurich (ETHZ), Master Thesis
| The goal of the project is to develop and test a smart sock prototype for plantar pressure measurements. The smart sock contains textile based pressure sensors and a readout module. This technology can be used for plantar pressure monitoring in diverse wearable applications ranging from healthcare to sports. - Biomedical Engineering, Medical and Health Sciences
- Master Thesis
| The goal of the project is to assess the feasibility of using commercially available plantar pressure monitoring devices (so called smart insoles) on the diabetic population. Pressure ulcers are a common complication of the diabetic foot, and monitoring plantar pressure continuously is a potential measure of prevention. Diabetic patients are often prescribed personalized footwear (e.g., curved insoles that accommodate any deformity in the feet). This project aims at assessing the potential of the smart insoles available on the market to monitor plantar pressure in diabetic patients with such custom footwear. - Biomedical Engineering, Medical and Health Sciences
- Bachelor Thesis, Semester Project
| Following trauma or due to degeneration it can be necessary to replace one or more intervertebral discs with an implant, a so-called Total Disc Replacement (TDR). Such devices enable motion though surfaces articulating against each other. While this treatment is clinically successful, it is connected to considerable complication and reoperation rates. Therefore, we are optimizing the design of such an implant to address these issues.
While many different designs and design types have been proposed and are used in clinical practice, there is no consensus on what design or design type is the most beneficial. However, it is hypothesized, that replicating the situation that is present in healthy (asymptomatic) subjects as closely as possible, is optimal. Since the motions of the cervical spine are coupled (coupling of rotation and translation as well as multiple rotations) the optimal design of the articulating surfaces is not obvious. Therefore, this master’s thesis project aims at designing the implants articulating surfaces using parametric design optimization in LS-OPT based on finite element simulations. - Engineering and Technology
- Master Thesis
| We currently want to (i) elaborate the added value of a campus board that records the forces per limb, (ii) determine grasping phases for kinematic analyses of the phalanges more pragmatically than with 6DoF sensors, and (iii) drive forward a competition analysis based purely on video material. - Biomedical Engineering, Human Movement and Sports Science
- Internship, Semester Project
| Estimating human poses within global trajectories is critical for applications such as augmented reality and sports analytics, yet it often demands precisely calibrated cameras and significant computational efforts. With advancements in deep learning and pose estimation technologies, various models can be trained using 2D or 3D motion data. However, effectively integrating these models to predict and analyze human movement trajectories in a continuous and dynamic environment remains challenging. This project aims to create a robust system that estimates and predicts human poses accurately, facilitating advancements in dynamic pose analysis and real-world applications. - Information, Computing and Communication Sciences
- ETH Zurich (ETHZ), Lab Practice, Master Thesis, Semester Project
| Reaching and grasping an object of interest is a relatively simple
task that can be achieved robustly in case the object is equipped
with a simple handle and a visual marker. However, often the difficulty in the task originates from the rest of the environment.
The object may be placed in cluttered spaces with diverse obstacles
as well as dynamic entities, e.g. humans, other robots. As a result,
executing the task of reaching and grasping the object necessitates
collision-free motion control capabilities. - Intelligent Robotics, Robotics and Mechatronics
- Master Thesis, Semester Project
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