With the help of wearable robotics, the lower limb exoskeleton becomes a promising solution for spinal cord injury (SCI) patients to recover lower body locomotion ability. However, fewer exoskeleton gait planning methods can meet the needs of patient in real time, e.g., stride length or step width, etc., which may lead to human-machine incoordination, limit comfort, and increase the risk of falling. This work presents a human-exoskeleton-crutch system with the center of pressure (CoP)-based gait planning method to enable the balance control during the exoskeleton-assisted walking with crutches. The CoP generated by crutches and human-machine feet makes it possible to obtain the overall stability conditions of the system in the process of exoskeleton-assisted quasi-static walking, and therefore, to determine the next stride length and ensure the balance of the next step. Thus, the exoskeleton gait is planned with the guidance of stride length. It is worth emphasizing that the nominal reference gait is adopted as a reference to ensure that the trajectory of the swing ankle mimics the reference one well. This gait planning method enables the patient to adaptively interact with the exoskeleton gait. The online gait planning walking tests with five healthy volunteers proved the method’s feasibility. Experimental results indicate that the algorithm can deal with the sensed signals and plan the landing point of the swing leg to ensure balanced and smooth walking. The results suggest that the method is an effective means to improve human–machine interaction. Additionally, it is meaningful for the further training of independent walking stability control in exoskeletons for SCI patients with less assistance of crutches.

The accurate prediction of joint torque is required in various applications. Some traditional methods, such as the inverse dynamics model and the electromyography (EMG)-driven neuromusculoskeletal (NMS) model, depend on ground reaction force (GRF) measurements and involve complex optimization solution processes, respectively. Recently, machine learning methods have been popularly used to predict joint torque with surface electromyography (sEMG) signals and kinematic information as inputs. This study aims to predict lower limb joint torque in the sagittal plane during walking, using a long short-term memory (LSTM) model and Gaussian process regression (GPR) model, respectively, with seven characteristics extracted from the sEMG signals of five muscles and three joint angles as inputs. The majority of the normalized root mean squared error (NRMSE) values in both models are below $15\%$, most Pearson correlation coefficient (R) values exceed $0.85$, and most decisive factor ($R^2$) values surpass $0.75$. These results indicate that the joint prediction of torque is feasible using machine learning methods with sEMG signals and joint angles as inputs.

Due to some model uncertainties and unknown friction disturbances that exist in the 2-DOF lower limb exoskeleton, a linear extended state observer (LESO) is proposed to estimate the unmeasurable angular velocity of two joints and the lumped uncertainties caused by friction disturbance and hydraulic parametric uncertainties. Meanwhile, by using the Lyapunov technique, a sliding mode controller is designed to improve the dynamic performance and the steady state accuracy of two joint angle responses in human–exoskeleton cooperative motion. By regulating the sliding mode controller gain, both the system state errors and estimation errors of the LESO are reduced in an arbitrary boundary of zero neighborhood. Finally, the effectiveness of the proposed control scheme is verified with both simulation and experimental results for one operator-wearable test, to guarantee that the joint position tracking performance and human–exoskeleton impedance torques are suppressed in a satisfactory boundary.