###### Abstract
The motor control parameters are produced from able-bodied data and can be regulated. In [11], an adaptive stair ascent controller for an above-knee leg prosthesis was discussed. With the adaptation to the changed heights of different stairs, the amputee can climb stairs with preferred cadence and gait patterns. The control parameters can be adjusted without the help of amputees' experience. To adaptively control the motions of the leg prosthesis, a cost function is usually designed to tune the control parameters. In [12], researchers designed a cost function based on the metabolism of the subject to tune the control parameters of the exoskeleton joints. In [13], a data-driven based on the continuous-phase variable was used during the stance phase, and kinematic control was used during the swing phase. A cost function was built to optimize the impedance parameters under different walking conditions. In [14], different impedance controllers were used and compared to reduce the metabolic cost of the subject. The cost function was designed and optimized to tune the control parameters and balance the system weight and assistance efficiency. However, the search process for optimizing the cost function usually requires hours. In [15], experiments showed that the cost function based on the subject's metabolism is not suitable for real-time adaptation. In [16], the combined cost function based on both the tracking errors and the subject's metabolism was used to assess the subject's discomfort during walking. Hence, to achieve the real-time adaptation of the combined cost function, an effective tuning method is needed. In [17], the robot's motion pattern was regulated iteratively and automatically to minimize the physiological cost function. The real-time adaptive motion pattern generated various assisting forces when the subject walked on the ground with a leg prosthesis. The motion pattern parameters were searched using human-in-the-loop optimization. The physiological objective of human-in-the-loop optimization is to minimize the cost function to obtain a suitable assistance control method. Although the optimization algorithms were different in these studies, the adaptive control schemes were based on the metabolic cost function. The objective process of optimization is to minimize the metabolic state,as the amount of oxygen consumption can depict whether the leg prosthesis can assist the subject with a positive effect during walking [18]. However, it usually requires several hours to evaluate oxygen consumption. The cost function based on metabolic processes also contains massive amounts of noise [19]. Hence, other physiological responses should be chosen as objective costs. In [20], the muscle activity signal was selected as the optimization objective. Surface electromyography (sEMG) was used to measure energy consumption during muscle activity. The muscle activity signal was easily obtained and calculated in less time than the collection of metabolic costs. The signal of muscle activity has been adopted as the objective cost to regulate the control parameters of robotic leg prostheses [21]. In [22], the movement intention of the amputee was identified from the change in surface electromyography (sEMG). The performance of the classifier based on sEMG is not sufficiently accurate. Hence, multi-modal switching was adopted to combine sEMG with the signals from the inertial measurement unit (IMU) to distinguish between the stance and swing phases [23]. In [24], researchers presented a normal model for obtaining sEMG signals within a socket with soft and low-profile electrodes. The subject achieved a comfortable experience with the soft electrodes on level ground. The quantitative questionnaire characterized the comfort of the subject for soft electrodes. Qualitative analyses also suggest the feasibility of using soft electrodes for real-time sEMG data collection. In [25], a parallel classifier approach was proposed to recognize the motion intent of a subject.