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Reliability and Validity testing of an Individuation device for a clinical use
After a neurological injury (such as stroke), many patients suffer from impairment of the hand and finger function. Clinical assessments aim to measure and quantify those impairments for a better understanding and to specifically target those deficits in rehabilitation. One aspect of hand function, that is not truly understood yet is finger individuation: the ability to move one finger independently of the others. In a previously developed assessment device, we use force sensors attached to a hand module to measure this dexterous skill. This individuation device will be used in a clinical setting to measure neurological patients. But before it can routinely be put into practice, its reliability (in a test-retest setting) and validity must be proven.
This project is a collaboration between the Rehabilitation Engineering Laboratory (RELab) of ETH Zurich, the cereneo clinic (center for neurology and rehabilitation) and Lake Lucerne Institute (LLUI).
This project is a collaboration between the Rehabilitation Engineering Laboratory (RELab) of ETH Zurich, the cereneo clinic (center for neurology and rehabilitation) and Lake Lucerne Institute (LLUI).
The goal of this project is to test and evaluate the reliability and validity of a finger individuation assessment for the neurological rehabiltiation.
The goal of this project is to test and evaluate the reliability and validity of a finger individuation assessment for the neurological rehabiltiation.
- Test an existing individuation device for reliability and validity in able-bodied participants and neurological patients
- Data analysis of the collected measurements
- Test an existing individuation device for reliability and validity in able-bodied participants and neurological patients - Data analysis of the collected measurements
- Background in health science and technology, biomedical engineering, neuroscience, data science, or related field
- Basic coding skills for data analysis
- High social skills (especially in the clinic)
- Reliable, precise, and structured working
- Motivated, friendly and communicative approach to work
- Conceptional skills, independent working style
- Background in health science and technology, biomedical engineering, neuroscience, data science, or related field - Basic coding skills for data analysis - High social skills (especially in the clinic) - Reliable, precise, and structured working - Motivated, friendly and communicative approach to work - Conceptional skills, independent working style
To apply please send a cover letter outlining your motivation and experience in the field, CV and a transcript of records to anna.knill@hest.ethz.ch / anna.knill@lui.org. I am happy to answer any project related questions.
To apply please send a cover letter outlining your motivation and experience in the field, CV and a transcript of records to anna.knill@hest.ethz.ch / anna.knill@lui.org. I am happy to answer any project related questions.