Fingertip force estimation from forearm muscle electrical activity

Existing commercial hand prostheses can be controlled from the electrical activity (electromyogram or EMG) of remnant muscle tissue within the forearm, but are limited in function to one degree of freedom of proportional control. In a pilot study (N=3 subjects), we used least squares estimation to identify a model between forearm electrical activity recorded by high-resolution (64 channel) electrode arrays (applied over the flexor and, separately, extensor muscles of the forearm) to force in the four fingertips. Average errors ranged from 4.21 to 10.20 %MVCF (flexion maximum voluntary contraction), depending on the muscle contraction task performed, number of EMG electrodes in the model and the electrode montage selected. Results suggest that, at least for intact subjects, 2-4 degrees of freedom of proportional control are available from the EMG signals of the forearm.

[1]  Jiri Silny,et al.  Spatial Filtering of Noninvasive Multielectrode EMG: Part II-Filter Performance in Theory and Modeling , 1987, IEEE Transactions on Biomedical Engineering.

[2]  R.N. Scott,et al.  A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.

[3]  Nitish V. Thakor,et al.  Real-time myoelectric decoding of individual finger movements for a virtual target task , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  C. Disselhorst-Klug,et al.  Non-invasive approach of motor unit recording during muscle contractions in humans , 2000, European Journal of Applied Physiology.

[5]  Jiri Silny,et al.  Spatial Filtering of Noninvasive Multielectrode EMG: Part I-Introduction to Measuring Technique and Applications , 1987, IEEE Transactions on Biomedical Engineering.

[6]  Dario Farina,et al.  Blind separation of linear instantaneous mixtures of nonstationary surface myoelectric signals , 2004, IEEE Transactions on Biomedical Engineering.

[7]  F. E. Delagi Anatomical guide for the electromyographer , 2014 .

[8]  M.W. Jiang,et al.  EMG Signal Classification for Myoelectric Teleoperating a Dexterous Robot Hand , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[9]  M. Osman Tokhi,et al.  A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis , 2003, IEEE Transactions on Biomedical Engineering.

[10]  D. Atkins,et al.  Epidemiologic Overview of Individuals with Upper-Limb Loss and Their Reported Research Priorities , 1996 .

[11]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[12]  E. Delagi,et al.  Anatomical guide for the electromyographer : the limbs and trunk /by Edward F. Delagi [et al.] ; illustrated by Phyllis B. Hammond, Aldo O. Perotto, and Hugh Thomas , 2005 .

[13]  D. Farina,et al.  Simultaneous and Proportional Estimation of Hand Kinematics From EMG During Mirrored Movements at Multiple Degrees-of-Freedom , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Pu Liu,et al.  Identification of Constant-Posture EMG–Torque Relationship About the Elbow Using Nonlinear Dynamic Models , 2012, IEEE Transactions on Biomedical Engineering.

[15]  Nitish V. Thakor,et al.  Decoding of Individuated Finger Movements Using Surface Electromyography , 2009, IEEE Transactions on Biomedical Engineering.

[16]  G.F. Inbar,et al.  Classification of finger activation for use in a robotic prosthesis arm , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[17]  E. Clancy,et al.  Influence of advanced electromyogram (EMG) amplitude processors on EMG-to-torque estimation during constant-posture, force-varying contractions. , 2006, Journal of biomechanics.

[18]  Evelyn Morin,et al.  Optimal Electrode Configurations for Finger Movement Classification using EMG , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Patrick van der Smagt,et al.  Surface EMG in advanced hand prosthetics , 2008, Biological Cybernetics.

[20]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[21]  T. Kuiken,et al.  Quantifying Pattern Recognition—Based Myoelectric Control of Multifunctional Transradial Prostheses , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Nitish V. Thakor,et al.  Continuous decoding of finger position from surface EMG signals for the control of powered prostheses , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Rajesh P. N. Rao,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 1 Online Electromyographic Control of a Robotic , 2022 .

[24]  Ian D. Walker,et al.  Myoelectric teleoperation of a complex robotic hand , 1996, IEEE Trans. Robotics Autom..

[25]  B Hudgins,et al.  Myoelectric signal processing for control of powered limb prostheses. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[26]  P. Dario,et al.  Control of multifunctional prosthetic hands by processing the electromyographic signal. , 2002, Critical reviews in biomedical engineering.

[27]  R. Buschbacher Anatomical Guide for the Electromyographer: The Limbs and Trunk , 2007 .