DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients

Machine-learning-based coordination of powered ankle-foot orthosis and functional electrical stimulation for gait control

This study proposes a novel gait rehabilitation method that uses a hybrid system comprising a powered ankle-foot orthosis (PAFO) and FES, and presents its coordination control. The developed system provides assistance to the ankle joint in accordance with the degree of volitional participation of patients with post-stroke hemiplegia. The PAFO adopts the desired joint angle and impedance profile obtained from biomechanical simulation. The FES patterns of the tibialis anterior and soleus muscles are derived from predetermined electromyogram patterns of healthy individuals during gait and personalized stimulation parameters. The CNN-based estimation model predicts the volitional joint torque from the electromyogram of the patient, which is used to coordinate the contributions of the PAFO and FES. The effectiveness of the developed hybrid system was tested on healthy individuals during treadmill walking with and without considering the volitional muscle activity of the individual. The results showed that consideration of the volitional muscle activity significantly lowers the energy consumption by the PAFO and FES while providing adaptively assisted ankle motion depending on the volitional muscle activities of the individual. The proposed system has potential use as an assist-as-needed rehabilitation system, where it can improve the outcome of gait rehabilitation by inducing active patient participation depending on the stage of rehabilitation.

Automatic Hemiplegia Type Detection (Right or Left) Using the Levenberg-Marquardt Backpropagation Method

Hemiplegia affects a significant portion of the human population. It is a condition that causes motor impairment and severely reduces the patient’s quality of life. This paper presents an automatic system for identifying the hemiplegia type (right or left part of the body is affected). The proposed system utilizes the data taken from patients and healthy subjects using the accelerometer sensor from the RehaGait mobile gait analysis system. The collected data undergo a pre-processing procedure followed by a feature extraction stage. The extracted features are then sent to a neural network trained by the Levenberg-Marquardt backpropagation (LM-BP) algorithm. The experimental part of this research involved creating a custom-created dataset containing entries taken from ten healthy and twenty non-healthy subjects. The data were taken from seven different sensors placed in specific areas of the subjects’ bodies. These sensors can capture a three-dimensional (3D) signal using the accelerometer, magnetometer, and gyroscope device types. The proposed system used the signals taken from the accelerometers, which were split into 2-sec windows. The proposed system achieved a classification accuracy of 95.12% and was compared with fourteen commonly used machine learning approaches.

Musculoskeletal Rehabilitation: State-of-the-Art

Musculoskeletal rehabilitation has been receiving growing attention in the scientific research field taking into account the functional consequences of chronic pain that have been frequently underestimated and undertreated. The usual rehabilitative care of a wide range of diseases affecting physical function and independence in activities of daily living needs to be improved with novel approaches. In this scenario, the recent literature has highlighted the great advantages of multidisciplinary and comprehensive pain management. The Special Issue highlights the importance of advancements in musculoskeletal rehabilitation in terms of instrumental physical therapies, therapeutic exercise, osteopathic manual therapy, innovative approaches and the correlation with dentistry. Physicians should be aware of the presence of novel therapeutic approaches that are changing the clinical scenario of musculoskeletal rehabilitation.