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

• Published in: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2009; pp. 4755 - 4758
• Main Authors: Delis, A.L., de Carvalho, J.L.A., Borges, G.A., de Siqueira Rodrigues, S., dos Santos, I., da Rocha, A.F.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01-01-2009
• Subjects: Algorithms; Artificial Limbs; Cepstral analysis; cepstral coefficients; Electromyographic signals; Electromyography - methods; Entropy; Feature extraction; Frequency domain analysis; Humans; Kalman filter; Knee; Knee Joint - anatomy & histology; Leg; Pattern classification; Pattern recognition; Pattern Recognition, Automated; proprioceptive sensors; Prosthetics; Sensor fusion; Signal Processing, Computer-Assisted; transfemoral prostheses
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/IEMBS.2009.5334184

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.