A Bioinspired Feature-Projection-Based Approach to Electromyographic Pattern Recognition Based for High Dimensional Sparse Sensor Data

Giovanni Schiboni, Peidong Liang, Chenguang Yang, Liming Chen, Sanja Dogramadzi

    Research output: Contribution to conferencePaperpeer-review

    Abstract

    The paper presents an electromyographic pattern recognition for sensor fusion able to discern motions of hand with a small number of training samples. We propose a learning algorithm able to classify estimate class statistics from a limited training set size. A Wavelet Packet Decomposition performs feature extraction. A sparse Principal Component Analysis projects the features in a lower dimensionality space. Classification is performed through multi-layer Perceptron. We employed sparse Principal Component Analysis because it is insensitive to the curse dimensionality problem differently from standard Principal Component Analysis that fails to capture discriminatory information in low-variance sensor data. The approach mitigates drawbacks of the training data collection as time consumption and acquisition difficulties. The latter are particularly relevant in case of high degrees of user disability, in which long sessions of training become unfeasible due to stress and exertion.

    Conference

    Conference2015 IEEE 12th Intl. Conf. on Ubiquitous Intelligence and Computing, 2015 IEEE 12th Intl. Conf. on Autonomic and Trusted Computing and 2015 IEEE 15th Intl. Conf. on Scalable Computing and Communications and its Associated Workshops (UIC-ATC-ScalCom)
    Period10/08/1514/08/15

    Fingerprint Dive into the research topics of 'A Bioinspired Feature-Projection-Based Approach to Electromyographic Pattern Recognition Based for High Dimensional Sparse Sensor Data'. Together they form a unique fingerprint.

    Cite this