More natural and intuitive control is expected to maximize the auxiliary effect of the powered prosthetic leg for lower limb amputees. In order to realize the stable and flexible walking of prosthetic legs in different terrains according to human intention, a brain-computer interface (BCI) based on motor imagery (MI) is developed. For the raw electroencephalogram (EEG) signals, discrete wavelet transform (DWT) is utilized to extract the time-frequency domain features, which are used as the input signals of the common spatial pattern (CSP) to obtain the time-frequency-space domain features of EEG signals. Then, a support vector machine (SVM) classifier and a directed acyclic graph (DAG) structure are combined to classify multiclass imaginary tasks. According to the result of human intention recognition, the prosthetic leg performs the corresponding gait trajectory generated by coding the ground reaction force (GRF). In addition, a sensory feedback loop is established by functional electrical stimulation (FES), which feeds back the movement of the prosthetic leg to human in real time. The effectiveness and feasibility of the developed EEG-based volitional control of powered prosthetic legs have been validated by three subjects, all of whom were able to fulfill smoothly walking on the floor, ascending stairs, and descending stairs according to their own intentions using prosthetic legs.

In the paper, the development, control, and preliminary evaluation of the human-robot coupled walking exoskeleton for weight-support enhancement are presented, which provides the assistance of abduction/adduction and flexion/extension for the hip joint, and the flexion/extension for knee joint of human legs during walking. The trajectory generation strategy for the walking exoskeleton utilizes the inverted pendulum approximation. Considering human in the control loop, the periodic walking trajectory and uncertainties with known periods, we propose a human-cooperative adaptive fuzzy strategy combing virtual tunnels that allows human subjects to change the movement timing of their legs and produce a physiological path. The strategy does not need human-robot coupled model, and the designed compliant virtual constraints keep the legs of the subject within the constrained tunnel around the expected path. The subjects with the assistance of supporting torques can walk effortlessly along the spatial path. The path control strategy has been verified with two healthy subjects. The recorded kinematic data demonstrated that the participants are able to achieve stable walking with larger spatio-temporal variability.