Analysing the effect of robotic gait on lower extremity muscles and classification by using deep learning

Abstract

Robotic gait training helps the nervous system recover and strengthen weak muscle groups. Many studies in the literature show that applying robotic gait rehabilitation to patients with neurological disorders such as Multiple Sclerosis (MS), Stroke and Spinal Cord Injection (SCI) effectively restores gait ability. In contrast to the studies in the literature that included only healthy individuals, both the control and patient groups were formed and detailed analyses were carried out for both groups. In this study, EMG signals in GMA, GME, ILP, BF, VM, MG, TA muscles were recorded simultaneously with a different electrode placement during robotic gait for the first time in literature and then a location that prevents a phase shift was presented. The classification performance has also been increased by removing 26 different attribute parameters like time, frequency and statistics from the signals instead of gait studies with a maximum of 12–16 traits extraction. The extracted features were classified with the approaches Multilayer Perceptron Neural Networks (MLP), Support Vector Machines (SVM), K-Nearest Neighbourhood algorithm (KNN), Random Forest Classification Algorithm (RF) and Deep Learning and then a detailed performance comparison have been realized. Among the approaches compared the Stochastic Gradient Optimization Algorithm-based deep learning structure produced the best performance with 98.5714% accuracy. It was also seen that it is essential to plan the exoskeleton and the robotic gait pattern suitable for patients’ disease state and muscle activation.

Keywords:

• Lower extremity
• gait analysis
• EMG
• deep learning
• classification

Acknowledgements

We would like to thank Erciyes Scientific Research Projects Office for financial support. In addition, we would like to thank Adana City Hospital Management for its contribution to the Ethics Committee. In particular, we would like to thank the Renaissance Business Services who own and allow the use of the RoboGait device and Fimer Private Health Services responsible for the use of the device. We would like to thank Dr. Turgay Özcüler and Dr. Halit Fidancı for their contribution to the evaluation of EMG signals.

Authors’ contributions

The authors contribute equally to the article.

Disclosure statement

The authors declare that they have no conflict of interest.

Additional information

Funding

Erciyes University Scientific Research Projects Coordinator and Erciyes Calibration Research Centre support this work with FDK2018-8375 project code.