Differentiating Variations in Thumb Position From Recordings of the Surface Electromyogram in Adults Performing Static Grips, a Proof of Concept Study

Hand gesture and grip formations are produced by the muscle synergies arising between extrinsic and intrinsic hand muscles and many functional hand movements involve repositioning of the thumb relative to other digits. In this study we explored whether changes in thumb posture in able-body volunteers can be identified and classified from the modulation of forearm muscle surface-electromyography (sEMG) alone without reference to activity from the intrinsic musculature. In this proof-of-concept study, our goal was to determine if there is scope to develop prosthetic hand control systems that may incorporate myoelectric thumb-position control. Healthy volunteers performed a controlled-isometric grip task with their thumb held in four different opposing-postures. Grip force during task performance was maintained at 30% maximal-voluntary-force and sEMG signals from the forearm were recorded using 2D high-density sEMG (HD-sEMG arrays). Correlations between sEMG amplitude and root-mean squared estimates with variation in thumb-position were investigated using principal-component analysis and self-organizing feature maps. Results demonstrate that forearm muscle sEMG patterns possess classifiable parameters that correlate with variations in static thumb position (accuracy of 88.25 ± 0.5% anterior; 91.25 ± 2.5% posterior musculature of the forearm sites). Of importance, this suggests that in transradial amputees, despite the loss of access to the intrinsic muscles that control thumb action, an acceptable level of control over a thumb component within myoelectric devices may be achievable. Accordingly, further work exploring the potential to provide myoelectric control over the thumb within a prosthetic hand is warranted.

[1]  Dario Farina,et al.  Proportional estimation of finger movements from high-density surface electromyography , 2016, Journal of NeuroEngineering and Rehabilitation.

[2]  Marco Santello,et al.  Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control , 2017, Front. Neurol..

[3]  Dinesh K Kumar,et al.  Selection of suitable hand gestures for reliable myoelectric human computer interface , 2015, Biomedical engineering online.

[4]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[5]  Dario Farina,et al.  Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control , 2014, IEEE Transactions on Biomedical Engineering.

[6]  Michael Goldfarb,et al.  Multiclass Real-Time Intent Recognition of a Powered Lower Limb Prosthesis , 2010, IEEE Transactions on Biomedical Engineering.

[7]  Adrian D. C. Chan,et al.  A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses , 2005, IEEE Transactions on Biomedical Engineering.

[8]  Patrick van der Smagt,et al.  Evidence of muscle synergies during human grasping , 2013, Biological Cybernetics.

[9]  Craig Sherstan,et al.  Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching , 2016, Prosthetics and orthotics international.

[10]  Kengo Ohnishi,et al.  Neural machine interfaces for controlling multifunctional powered upper-limb prostheses , 2007, Expert review of medical devices.

[11]  Nitish V. Thakor,et al.  Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning , 2018, IEEE Transactions on Biomedical Engineering.

[12]  Pornchai Phukpattaranont,et al.  Feature reduction and selection for EMG signal classification , 2012, Expert Syst. Appl..

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

[14]  R.Fff. Weir,et al.  A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Y. Ayaz,et al.  Modified SOM based intelligent semi-autonomous navigation system , 2012, 11th Symposium on Neural Network Applications in Electrical Engineering.

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

[17]  M. Hepp-Reymond,et al.  EMG activation patterns during force production in precision grip , 2004, Experimental Brain Research.

[18]  C. D. De Luca,et al.  Behaviour of human motor units in different muscles during linearly varying contractions , 1982, The Journal of physiology.

[19]  D. Childress,et al.  Myoelectric control , 2006, Medical and biological engineering.

[20]  Jie Tang,et al.  Coordination of thumb joints during opposition. , 2007, Journal of biomechanics.

[21]  T. Kohonen Self-organized formation of topographically correct feature maps , 1982 .

[22]  M Controzzi,et al.  Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  Katsunori Shimohara,et al.  EMG pattern analysis and classification by neural network , 1989, Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics.

[24]  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.

[25]  Suncheol Kwon,et al.  Real-time thumb-tip force predictions from noninvasive biosignals and biomechanical models , 2012, International Journal of Precision Engineering and Manufacturing.

[26]  Hong Liu,et al.  EMG pattern recognition and grasping force estimation: Improvement to the myocontrol of multi-DOF prosthetic hands , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[27]  S P Arjunan,et al.  A machine learning based method for classification of fractal features of forearm sEMG using Twin Support vector machines , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[28]  T Laurell,et al.  Myoelectric control of a computer animated hand: A new concept based on the combined use of a tree-structured artificial neural network and a data glove , 2006, Journal of medical engineering & technology.

[29]  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.

[30]  Dario Farina,et al.  Enhanced EMG signal processing for simultaneous and proportional myoelectric control , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[31]  Konrad Paul Kording,et al.  The statistics of natural hand movements , 2008, Experimental Brain Research.

[32]  Evelyn Morin,et al.  Myoelectric Signal Processing , 2006 .