multi-channel semg-based joint angles estimation of lower limbs utilizing bidirectional recurrent neural network

Nowadays, rehabilitative robots, which have received more attention in the field of rehabilitation, can help patients in the rehabilitation training and reduce therapist workload. this paper suggests the use of surface electromyography (semg) signals and a bidirectional neural network (brnn) for the estimation of the joint angles of lower limbs. the input of brnn is the preprocessed semg signals and its outputs are the estimated joint angles of knee, ankle, and hip. in order to prove the usefulness of the brnn, four normal and healthy subjects and two patients suffering from spinal cord injury (sci) took part in the experimental tests. the healthy subjects exercised two movement modes including leg extension and treadmill at various loads and speeds, while the sci subjects conducted only the treadmill exercise. to record useful information, seven leg muscles were used and then the hip, knee, and ankle joint angles were acquired at the same time. the experimental results showed the satisfactory performance of the proposed method in the estimation of joint angles by employing surface electromyography signals for both groups. the proposed estimation method can be used to control the rehabilitation robot of sci subjects based on semg signals.