A wireless wearable sEMG and NIRS acquisition system for an enhanced human-computer interface

Surface electromyography (sEMG) is extensively explored in human-computer interface (HCI); complementary to the electrophysiological activity of the muscles, the hemodynamic information that measured from near infrared spectroscopy (NIRS) is less investigated. Properly combining the sEMG and NIRS would provide a novel approach for HCI applications. This paper presents a multi-channel wireless wearable sEMG and NIRS acquisition system aiming for enhanced human-computer interaction, by providing more information about the muscle activity for subject's motor intention decoding. Extensive tests were carried out to evaluate the system performance. It showed that this novel system proved to be able to capture sEMG signals similar to those of the commercialized sEMG acquisition devices, and had a comparable NIRS sensor performance. Furthermore, simultaneously recording of sEMG and NIRS signals, the system had shown the ability to provide more information about the muscle activities for a better HCI performance. The classification accuracy of 13 hand gesture motions was significantly (P<;0.001) improved by using combined sEMG and NIRS features comparing to sEMG or NIRS features individually, suggesting that the proposed sEMG and NIRS system could be potentially available for an enhanced HCI.

[1]  Kevin B. Englehart,et al.  A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.

[2]  S. Bunce,et al.  Functional near-infrared spectroscopy , 2006, IEEE Engineering in Medicine and Biology Magazine.

[3]  Othman Omran Khalifa,et al.  EMG signal classification for human computer interaction: a review , 2009 .

[4]  M. Wolf,et al.  Wireless miniaturized in-vivo near infrared imaging. , 2008, Optics express.

[5]  Yoshiyuki Sankai,et al.  Power assist control for walking aid with HAL-3 based on EMG and impedance adjustment around knee joint , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Babak Shadgan,et al.  Wireless near-infrared spectroscopy of skeletal muscle oxygenation and hemodynamics during exercise and ischemia , 2009 .

[7]  B. Chance,et al.  A novel method for fast imaging of brain function, non-invasively, with light. , 1998, Optics express.

[8]  Luciano Boquete,et al.  A portable wireless biometric multi-channel system , 2012 .

[9]  H. Hashimoto,et al.  Driving Electric Car by Using EMG Interface , 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.

[10]  C. J. Luca,et al.  SURFACE ELECTROMYOGRAPHY : DETECTION AND RECORDING , 2022 .

[11]  N. Hogan,et al.  Customized interactive robotic treatment for stroke: EMG-triggered therapy , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Ching-Hsing Luo,et al.  Wireless biopotential acquisition system for portable healthcare monitoring , 2011, Journal of medical engineering & technology.

[13]  Conor O'Mahony,et al.  Preliminary technological assessment of microneedles-based dry electrodes for biopotential monitoring in clinical examinations , 2012 .

[14]  Toshio Tsuji,et al.  A human-assisting manipulator teleoperated by EMG signals and arm motions , 2003, IEEE Trans. Robotics Autom..

[15]  Andreas Attenberger,et al.  Modeling and Visualization of Classification-Based Control Schemes for Upper Limb Prostheses , 2012, 2012 IEEE 19th International Conference and Workshops on Engineering of Computer-Based Systems.

[16]  Jacob Rosen,et al.  A myosignal-based powered exoskeleton system , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[17]  Wanderley Cardoso Celeste,et al.  Human–machine interface based on muscular and brain signals applied to a robotic wheelchair , 2007 .

[18]  R. Maestri,et al.  Preliminary study of muscle contraction assessment by NIR spectroscopy , 1998, European Conference on Biomedical Optics.

[19]  Andreas Attenberger,et al.  Prostheses Control with Combined Near-Infrared and Myoelectric Signals , 2011, EUROCAST.

[20]  M. Swiontkowski Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms , 2010 .

[21]  Carlo J. De Luca,et al.  Physiology and Mathematics of Myoelectric Signals , 1979 .

[22]  Stefan Herrmann,et al.  Fusion of myoelectric and near-infrared signals for prostheses control , 2010 .

[23]  Xinjun Sheng,et al.  A new feature extraction method based on autoregressive power spectrum for improving sEMG classification , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[24]  Toshiyuki Kondo,et al.  Proposal of anticipatory pattern recognition for EMG prosthetic hand control , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.