(1) Background: The success of physiotherapy depends on the regular and correct performance of movement exercises. A system that automatically evaluates these could support the 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 IMUs, a dataset of four Functional Movement Screening (FMS) exercises is recorded. Exercise execution is evaluated by physiotherapists using the FMS criteria. This dataset is used to train a neural network that assigns the correct FMS score to an exercise repetition. We use an architecture consisting of CNN, LSTM and Dense layers. Based on this framework, we apply various methods to optimize the performance of the network. For the optimization, we perform a extensive hyperparameter optimization. In addition, we are comparing different CNN structures that have been specifically adapted for use with IMU data. Finally, the developed network is trained with the data of different FMS exercises and the performance is compared. (3) Results: The evaluation shows that the presented approach achieves a convincing performance in the classification of unknown repetitions of already known subjects. However, the trained network is yet unable to achieve consistent performance on the data of a previously unknown subjects. Additionally, it can be seen that the performance of the network differs significantly depending on the exercise it is trained for.
翻译:(1) 背景:物理疗法的成功取决于运动练习的正常和正确性能。自动评估这些功能的系统可以支持治疗。这个领域的以往方法很少依赖深学习方法,也没有充分利用其潜力。 (2) 方法:使用由17个IMU组成的测量系统,这是四个功能运动筛选(FMS)练习的数据集;由物理治疗师使用FMS标准来评估运动执行情况。该数据集用于培训神经网络,将正确的FMS评分用于重复练习。我们使用由CNN、LSTM和Dense层组成的结构。根据这个框架,我们采用各种方法优化网络的性能。为了优化,我们进行了广泛的超参数优化。此外,我们正在比较专门为使用IMU数据而调整的不同CNN结构。最后,对发达的网络进行了培训,使用不同的FMS演练数据进行对比。(3)结果:评价表明,所提出的方法在对已知的题目进行分类时取得了令人信服的业绩。但是,在经过培训的网络中,在经过培训的科目上仍无法取得一致的成绩。