Determination of kinematic variables, force and pressure distributions, as well as EEG and surface EMG data during the performance of physiotherapeutic and gymnastic exercises, as well as gait analysis, for the application and evaluation of machine learning (ML) and deep learning (DL) algorithms.
- Conditions
- Healthy Volunteers
- Registration Number
- DRKS00027259
- Lead Sponsor
- Technische Hochschule Ulm
- Brief Summary
(1) Background: The success of physiotherapy depends on the regular and correct unsupervised performance of movement exercises. A system that automatically evaluates these exercises could increase effectiveness and reduce risk of injury in home based therapy. Previous approaches in this area rarely rely on deep learning methods and do not yet fully use their potential. (2) Methods: Using a measurement system consisting of 17 inertial measurement units, a dataset of four Functional Movement Screening exercises is recorded. Exercise execution is evaluated by physiotherapists using the Functional Movement Screening criteria. This dataset is used to train a neural network that assigns the correct Functional Movement Screening score to an exercise repetition. We use an architecture consisting of convolutional, long-short-term memory and dense layers. Based on this framework, we apply various methods to optimize the performance of the network. For the optimization, we perform an extensive hyperparameter optimization. In addition, we are comparing different convolutional neural network structures that have been specifically adapted for use with inertial measurement data. To test the developed approach, it is trained on the data from different Functional Movement Screening exercises and the performance is compared on unknown data from known and unknown subjects. (3) Results: The evaluation shows that the presented approach is able to classify unknown repetitions correctly. However, the trained network is yet unable to achieve consistent performance on the data of previously unknown subjects. Additionally, it can be seen that the performance of the network differs depending on the exercise it is trained for. (4) Conclusions: The present work shows that the presented deep learning approach is capable of performing complex motion analytic tasks based on inertial measurement unit data. The observed performance degradation on the data of unknown subjects is comparable to publications of other research groups that relied on classical machine learning methods. However, the presented approach can rely on transfer learning methods, which allow to retrain the classifier by means of a few repetitions of an unknown subject. Transfer learning methods could also be used to compensate for performance differences between exercises.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Complete
- Sex
- All
- Target Recruitment
- 30
No relevant pre-existing conditions that limit the function of the musculoskeletal system in relation to the defined range of motion exercises.
Diseases of the musculoskeletal system, unhealed injuries, recent surgeries, leg length discrepancies, disorders of the sense of balance.
Study & Design
- Study Type
- observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method The machine learning systems will be trained on the basis of the collected measurement data in order to be able to investigate to what extent an evaluation of the exercise performance is possible with this system. For this purpose, kinematic variables, force and pressure distributions, as well as EEG and surface EMG data will be collected.
- Secondary Outcome Measures
Name Time Method The quality of the acquired data set can be defined on the basis of a statistical evaluation of the measured variables and assigned exercise ratings.