This paper presents a robotic transfemoral prosthesis that consists of an electrohydraulic knee and a motor-driven ankle. The electrohydraulic knee includes a controllable hydraulic damper to provide stance control and an electrical actuator to provide swing assistance. The motor-driven ankle provides damping adaptation for different terrains. The embedded finite-state based controller enables the knee--ankle prosthesis to facilitate the amputee's ambulation on five different terrains. The proposed knee is lightweight and compact, with low electrical energy consumption. Experimental results demonstrate the potential advantages of the proposed transfemoral prosthesis compared with the hydraulic prosthesis daily used by the subject.

Feature extraction, as an important method for extracting useful information from surface electromyography (SEMG), can significantly improve pattern recognition accuracy. Time and frequency analysis methods have been widely used for feature extraction, but these methods analyze SEMG signals only from the time or frequency domain. Recent studies have shown that feature extraction based on time-frequency analysis methods can extract more useful information from SEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwell transform (S-transform) to improve hand movement recognition accuracy from forearm SEMG signals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vector from forearm SEMG signals. Second, to reduce the amount of calculations and improve the running speed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of the feature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is used for recognizing hand movements. Experimental results show that the proposed feature extraction based on the S-transform analysis method can improve the class separability and hand movement recognition accuracy compared with wavelet transform and power spectral density methods.

This paper presents an approach for passivity-based bipedal robots to achieve stable walking on uneven terrain. A powered two-dimensional seven-link walking model with flat feet and compliant ankles has been proposed to analyze and simulate the walking dynamics. We further describe an optimization based control method, which uses optimized hip actuation and ankle compliance as control parameters of bipedal walking. Satisfactory results of simulations and real robot experiments show that the passivity-based walker can achieve stable bipedal walking with larger ground disturbance by the proposed method in view of stability and efficiency.