PhD Student/Post-Doctoral Researcher in Human Movement Analysis and Real-time Movement Feedback for Personalized Neurorehabilitation in Zürich

The Rehabilitation Engineering Laboratory (RELab) at ETH Zürich is part of the Department of Health Sciences and Technology. The RELab is an interdisciplinary group with competences in mechanical and electrical engineering, movement science, neurorehabilitation and neuroscience. We apply robotics, wearable sensor technologies and non-invasive neuroimaging to explore, assess and restore sensorimotor function. Our goal is to promote sensorimotor recovery following neurological injury and to develop assistive technologies to compensate for remaining deficits. 

Cereneo Foundation is a non-profit research center with a research focus of 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 center to ensure true multi-stakeholder integration within each research projects. 

To support our ongoing research projects and strengthen our position as a leading research group in the field of human movement analysis and real-time movement feedback for motor improvement in neurological patients, we are looking for a PhD Student or a Postdoctoral Researcher with experience in human movement data collection and analysis (kinematic, kinetic, physiological signals), bioengineering or biomedical engineering, data mining and machine learning approaches, (bio)statistical analysis. The position will be shared between RELab (50%) and cereneo foundation (50%).

Gait deficits are one of the leading causes of dissatisfaction after neurorehabilitation and have long-ranging implications for mobility, secondary complications, and reduced quality of life. Through targeted rehabilitation motor improvements in gait are possible, and these frequently generalize to improvements in walking speed. Typically, practitioners target their rehabilitation practice based on implicit knowledge and unstructured pattern matching. In parallel, there is a growing body of evidence that real-time feedback can be beneficial to (re-) learning motor patterns after stroke or in Parkinson’s disease.

Within a large clinical research project, StimuLOOP, we will develop and investigate an intervention that objectively performs the steps of movement assessment, rehabilitation target ranking, and movement-based feedback provision in a controlled and objective manner. However, there is little comparison between different feedback designs and modalities. The aim of this line of research is to create personalized feedback scenarios and strategies and to investigate these in terms of adaptation and consolidation. Finally, we will investigate the generalizability of these interventions to walking quality, speed and to real world activity.

During training, while patients are walking on a treadmill, we will design a therapy approach where we present real time feedback of their movement quality on a large immersive projection, per haptic fields, and potentially per acoustics. In the field of neurorehabilitation, there is no general consensus that governs the provision of feedback. Patients have a large range of motor and sensory impairments and a varying capacity for integration of secondary information during a primary task. Your role in the large project will be to create personalized feedback scenarios and investigate the principles that underly the provision of effective feedback. You will use our real-time motion capture system and immersive projection to design and clinically validate different visual feedback tools in co-creation processes with patients and caregivers. In a second step you will extend these experiments to haptic fields and auditory soundscapes. Your work will contribute to a generalizable model of effective, personalized feedback provision for motor relearning.

Your tasks will include:

• Collection of movement data in healthy and clinical populations (stroke and Parkinson) using optical motion tracking and wearable sensors
• Design and Development of Virtual 3D environments (scenes and assets) that enable multifaceted visualization of biological data streams into visual stimuli (AR, VR, and projector solutions)
• Design, implement and validate scalable feedback strategies (hardware and software) based on motor learning principles using visual, haptic, and auditory tools
• Supervise Msc/PhD students
• Actively contribute to grant writing and preparation of scientific publications

• Master degree or Doctoral degree and extensive practical experience in human movement analysis, applied data science, neuroscience/neurorehabilitation, bioengineering or biomedical engineering, or other fields directly related to the application of digital health technologies for assessment of sensorimotor impairments in neurological patients
• Advanced knowledge in the use of motion capture, and sensor-based technologies (e.g., IMU) for assessment of human movements. Experience with gait analysis, including clinical interpretation, is an advantage
• Knowledge in (bio)statistical analysis, machine learning and modelling applied to biomedical/neurorehabilitation fields
• Interdisciplinary expertise involving data collection with human participants and their analysis, as well as preparation and coordination of clinical trials
• Experience in collaboration with clinicians and patients, and interest in closely collaborating with these partners
• As Postdoctoral researcher: Outstanding publication record in the field of human movement data analysis, data science, computational modeling, or neurorehabilitation/neuroscience
• As Postdoctoral researcher: Excellent management skills and experience with supervising students

• A unique and stimulating work environment split between two leading research groups in the field of technology-based assessments and neurorehabilitation
• Exciting technology platforms, already ongoing clinical studies, and large datasets that will be made available
• Direct exposure and deep integration with a clinical research environment, incorporating a wide range of practitioners and patients
• Competitive salary according to ETH standards for PhD students/Postdoctoral Researchers