Subject study to determine kinematic variables, force and pressure distributions, as well as surface electromyography (EMG) data during the performance of physiotherapeutic and gymnastic exercises, as well as gait analyses, for the application and evaluation of machine learning (ML) and deep learning (DL) algorithms.
- Conditions
- Acquisition of measurement data for the training of neural networks for the assessment of gait patterns and physiotherapeutic exercises for forefoot dorsiflexion weakness.
- Registration Number
- DRKS00034705
- Lead Sponsor
- Technische Hochschule Ulm
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Pending
- Sex
- All
- Target Recruitment
- 50
Inclusion Criteria
Signed and dated declaration of consent from the subject.
Exclusion Criteria
- Known allergic reaction to kinesio tapes / adhesives
- Presence of an acute general illness or an orthopaedic illness that prevents participation.
- Pregnancy
Study & Design
- Study Type
- observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method The machine learning systems are to be trained on the basis of the measurement data collected in order to be able to analyse the extent to which an automated assessment is possible with this system. The defined measurement data is collected for this purpose.
- Secondary Outcome Measures
Name Time Method The quality of the recorded data set can be defined on the basis of a statistical evaluation of the measured variables and assigned labels.