A deep learning-based comprehensive robotic system for lower limb rehabilitation

In the modern era, a significant percentage of people around the world suffer from knee pain-related problems. ‘Knee pain’ can be alleviated by performing knee rehabilitation exercises in the correct posture on a regular basis. In our research, an attention mechanism-based CNN-TLSTM (Convolution Neural Network-tanh Long Sort-Term Memory) network has been proposed for assessing the knee pain level of a person. Here, electroencephalogram (EEG) signals of the frontal, parietal, and temporal lobes, electromyography (EMG) signals of the hamstring and quadriceps muscles, and knee bending angle have been used for knee pain detection. First, the CNN network has been utilized for automated feature extraction from the EEG, knee bending angle, and EMG data, and subsequently, the TLSTM network has been used as a classifier. The trained CNN-TLSTM model can classify the knee pain level of a person into five categories, namely no pain, low pain, medium pain, moderate pain, and high pain, with an overall accuracy of 95.88 %. In the hardware part, a prototype of an automated robotic knee rehabilitation system has been designed to help a person perform three rehabilitation exercises, i.e., sitting knee bending, straight leg rise, and active knee bending, according to his/her pain level, without the presence of any physiotherapist. The novelty of our research lies in (i) designing a novel deep learning-based classifier model for broadly classifying knee pain into five categories, (ii) introducing attention mechanism into the TLSTM network to boost its classification performance, and (iii) developing a user-friendly rehabilitation device for knee rehabilitation.