Department of Health Sciences and TechnologyAcronym | D-HEST | Homepage | http://www.hest.ethz.ch/ | Country | Switzerland | ZIP, City | | Address | | Phone | | Type | Academy | Parent organization | ETH Zurich | Current organization | Department of Health Sciences and Technology | Child organizations | |
Open OpportunitiesAccurate non-invasive assessment modalities that incorporate both scapular motion and its morphology are currently unavailable, presenting a clear need for sustainable clinical application. To address this need, the Laboratory for Movement Biomechanics (LMB) utilizes a unique optical 4D scanning system (SLOT) to estimate the underlying anatomical structures using non-invasive structured light to produce high-quality images of the human skin surface, both statically and dynamically. By utilizing the clear cutaneous surface contours surrounding the scapula, the application of this technology to the shoulder joint could allow a novel non-invasive and dynamic approach for estimating scapular kinematics that overcomes the challenges associated with soft-tissue artifacts. The key challenge in the development of this approach is the precise identification and tracking of relevant scapula landmarks, as well as soft tissue artifacts, all of which are expected to affect the accuracy of the SLOT-measured kinematics. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis
| This master’s thesis is dedicated to developing an advanced nutrition tracking system for hospitals, integrating QR-code recognition and structured light camera technology. The focus is to significantly enhance the precision of food volume measurements and patient meal tracking with machine learning, thereby improving nutritional monitoring accuracy. - Computer Vision, Medical and Health Sciences, Software Engineering
- Master Thesis
| Study State-Space Models (SSMs) within the realm of Reinforcement Learning (RL) and ideally apply it in Robotics field. - Computer Vision, Intelligent Robotics, Knowledge Representation and Machine Learning
- Master Thesis
| This project focuses on utilizing various techniques for Video to Events generation. - Computer Vision
- 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
| The remarkable agility of animals, characterized by their rapid, fluid movements and precise interaction with their environment, serves as an inspiration for advancements in legged robotics. Recent progress in the field has underscored the potential of learning-based methods for robot control. These methods streamline the development process by optimizing control mechanisms directly from sensory inputs to actuator outputs, often employing deep reinforcement learning (RL) algorithms. By training in simulated environments, these algorithms can develop locomotion skills that are subsequently transferred to physical robots. Although this approach has led to significant achievements in achieving robust locomotion, mimicking the wide range of agile capabilities observed in animals remains a significant challenge. Traditionally, manually crafted controllers have succeeded in replicating complex behaviors, but their development is labor-intensive and demands a high level of expertise in each specific skill. Reinforcement learning offers a promising alternative by potentially reducing the manual labor involved in controller development. However, crafting learning objectives that lead to the desired behaviors in robots also requires considerable expertise, specific to each skill.
- Information, Computing and Communication Sciences
- Master Thesis
| Humanoid robots, designed to mimic the structure and behavior of humans, have seen significant advancements in kinematics, dynamics, and control systems. Teleoperation of humanoid robots involves complex control strategies to manage bipedal locomotion, balance, and interaction with environments. Research in this area has focused on developing robots that can perform tasks in environments designed for humans, from simple object manipulation to navigating complex terrains. Reinforcement learning has emerged as a powerful method for enabling robots to learn from interactions with their environment, improving their performance over time without explicit programming for every possible scenario. In the context of humanoid robotics and teleoperation, RL can be used to optimize control policies, adapt to new tasks, and improve the efficiency and safety of human-robot interactions. Key challenges include the high dimensionality of the action space, the need for safe exploration, and the transfer of learned skills across different tasks and environments. Integrating human motion tracking with reinforcement learning on humanoid robots represents a cutting-edge area of research. This approach involves using human motion data as input to train RL models, enabling the robot to learn more natural and human-like movements. The goal is to develop systems that can not only replicate human actions in real-time but also adapt and improve their responses over time through learning. Challenges in this area include ensuring real-time performance, dealing with the variability of human motion, and maintaining stability and safety of the humanoid robot.
- Information, Computing and Communication Sciences
- Master Thesis
| In recent years, advancements in reinforcement learning have achieved remarkable success in teaching robots discrete motor skills. However, this process often involves intricate reward structuring and extensive hyperparameter adjustments for each new skill, making it a time-consuming and complex endeavor. This project proposes the development of a skill generator operating within a continuous latent space. This innovative approach contrasts with the discrete skill learning methods currently prevalent in the field. By leveraging a continuous latent space, the skill generator aims to produce a diverse range of skills without the need for individualized reward designs and hyperparameter configurations for each skill. This method not only simplifies the skill generation process but also promises to enhance the adaptability and efficiency of skill learning in robotics. - Engineering and Technology, Information, Computing and Communication Sciences
- Master Thesis
| Knee kinematics is critical for diagnosing pathologies such as osteoarthritis and providing guidance for implant design. Estimating knee kinematics requires aligning a model with a target X-ray image. This estimation process, often implemented by human labor, can be very time-consuming. This research aims to use a deep learning network to estimate the pose (kinematics) from X-ray images, partially replacing manual labor. Such a network should predict a pose from a current fluoroscopic image. By the end of this project, a robust pipeline should be completed, achieving baseline performance to provide convincing pose estimation for images from different modalities (single-plane system & dual-plane system; natural bone model & implant model). - Biomechanics, Biomedical Engineering, Computer Vision
- ETH Zurich (ETHZ), Internship, Master Thesis, Semester Project
| The goal of the project is to develop and test a smart sock prototype for plantar pressure measurements. Existing previously developed textile pressure sensors are to be integrated in a standard sock. 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
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