Fusion of electromyographic signals with proprioceptive sensor data in myoelectric pattern recognition for control of active transfemoral leg prostheses.

This paper presents a myoelectric knee joint angle estimation algorithm for control of active transfemoral prostheses, based on feature extraction and pattern classification. The feature extraction stage uses a combination of time domain and frequency domain methods (entropy of myoelectric signals and cepstral coefficients, respectively). Additionally, the methods are fused with data from proprioceptive sensors (gyroscopes), from which angular rate information is extracted using a Kalman filter. The algorithm uses a Levenberg-Marquardt neural network for estimating the intended knee joint angle. The proposed method is demonstrated in a normal volunteer, and the results are compared with pattern classification methods based solely on electromyographic data. The use of surface electromyographic signals and additional information related to proprioception improves the knee joint angle estimation precision and reduces estimation artifacts.

Development of a microcontrolled bioinstrumentation system for active control of leg prostheses.

This article describes the design of a microcontrolled bioinstrumentation system for active control of leg prostheses, using 4-channel electromyographic signal (EMG) detection and a single-channel electrogoniometer. The system is part of a control and instrumentation architecture in which a master processor controls the tasks of slave microcontrollers, through a RS-485 interface. Several signal processing methods are integrated in the system, for feature extraction (Recursive Least Squares), feature projection (Self Organizing Maps), and pattern classification (Levenberg-Marquardt Neural Network). The acquisition of EMG signals and additional mechanical information could help improving the precision in the control of leg prostheses.