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Rehabilitation Engineering Lab

Acronymrelab
Homepagehttp://www.relab.ethz.ch/
CountrySwitzerland
ZIP, City8045 Zurich
AddressBAA, Lengghalde 5
Phone+ 41 44 510 72 34
TypeAcademy
Top-level organizationETH Zurich
Parent organizationInstitute of Robotics and Intelligent Systems D-HEST
Current organizationRehabilitation Engineering Lab
Memberships
  • Max Planck ETH Center for Learning Systems
  • ETH Competence Center - Competence Center for Rehabilitation Engineering and Science (RESC)


Open Opportunities

Development and evaluation of an assessment and therapy device for foot peripheral neuropathy

  • ETH Zurich
  • Rehabilitation Engineering Lab

Peripheral neuropathy affects approximately 2% of the population (e.g. linked to diabetes, MS, chemotherapy, antibiotics…), resulting in motor and somatosensory impairments and pain (numbness, tingling, burning, stabbing) in peripheral body segments, primarily the feet. Sensory stimulation and mechanical mobilization of the foot/toes have been shown to positively affect pain and balance in peripheral neuropathy. The Rehabilitation Engineering Lab at ETH has pioneered technology for the assessment and therapy of somatosensory hand function, applied in stroke, CP and MS. The goal of this project is to transfer and validate this technology in foot peripheral neuropathy.

  • Engineering and Technology, Medical and Health Sciences
  • Master Thesis, Semester Project

Targeted Memory Reactivation

  • ETH Zurich
  • Rehabilitation Engineering Lab

Cereneo Foundation is a non-profit research centre with a research focus on developing and implementing technological solutions to improve neurorehabilitation outcomes. We work on digital, objective assessment solutions and their implementation into clinical routine. These are used to evaluate novel interventions applying a broad range of tools (fMRI, EEG, neurophysiology, movement analysis, robotics). The foundation works closely with a neurorehabilitation centre to ensure true multi-stakeholder integration within each research project. To support our ongoing projects, we are looking for an Intern with strong interest in human movement, motor learning, and memory processes. Solid knowledge in data analysis, theoretical knowledge of motor learning and consolidation processes, and an enthusiasm to work with human participants at the forefront of technology is a plus.

  • Engineering and Technology, Medical and Health Sciences
  • Internship

Comparison of inertial measurement units for capturing human movement

  • ETH Zurich
  • Rehabilitation Engineering Lab

Wearable sensors, such as inertial measurement units (IMUs), have become a viable tool in neurological rehabilitation and can be utilized in various ways. They can be used to assess motor impairments, guide motor recovery, and evaluate the effectiveness of interventions. Since IMUs are lightweight and easy to wear, they are particularly useful for measuring upper limb movement and can provide a wide range of kinematic and kinetic information, including movement smoothness, speed or range of motion. However, as there are various commercially available sensors with varying characteristics, deciding which one to choose becomes challenging. This study will assess each IMU's accuracy, precision, temporal characteristics and ease of use regarding wearability, data accessibility, streaming capabilities and configurability. This can provide valuable information for researchers and clinicians to select the appropriate IMU for their specific application, according to the research aim. To do so, an overview of the sensor characteristics and an assessment battery to compare the sensors are necessary.

  • Biomechanical Engineering, Electrical and Electronic Engineering, Human Movement and Sports Science, Mechanical and Industrial Engineering, Rehabilitation Engineering
  • Internship, Master Thesis, Semester Project

Towards personalized augmented feedback for gait: influence of modality and signal characteristics

  • ETH Zurich
  • Rehabilitation Engineering Lab

Personalized and intensive rehabilitation has been shown to be highly beneficial for stroke patients to recover mobility. However, the increasing patients-to-therapist ratio and the raise of healthcare costs limit its feasibility. In this context, technological assistance such as wearable augmented feedback is of special interest to support patients. Feedback has been shown to facilitate modification of gait and to improve rehabilitation outcomes. Yet, the design of feedback modalities is often an imposed choice and we lack insights on how feedback can be shaped to be more impactful. This project aims at filling this gap by investigating the impact of different signal stimuli on gait training.

  • Biomechanical Engineering, Biomechanics, Motor Control, Rehabilitation Engineering
  • Internship, Master Thesis
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