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

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 a...

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Bibliographic Details
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
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Summary: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.
ISSN:1094-687X
1557-170X
1558-4615
DOI:10.1109/IEMBS.2009.5334184