Classification of Upper limb phantom movements in transhumeral amputees using electromyographic and kinematic features

Abstract Recent studies have shown the ability of transhumeral amputees to generate surface electromyography (sEMG) patterns associated to distinct phantom limb movements of the hand, wrist and elbow. This ability could improve the control of myoelectric prostheses with multiple degrees of freedom (DoF). However, the main issue of these studies is that these ones record sEMG from sites that cannot always be integrated in a prosthesis socket. This study aims to evaluate the classification accuracy of eight main upper limb phantom movements and a no movement class in transhumeral amputees based on sEMG data recorded exclusively on the residual limb. A sub-objective of this study is to evaluate the impact of kinematic data on the classification accuracy. Five transhumeral amputees participated in this study. Classification accuracy obtained with an artificial neural network ranged between 60.9% and 93.0%. Accuracy decreased if the number of DoF considered in the classification increased, and/or if the phantom movements became more distal. Adding a kinematic feature produced an average increase of 4.8% in accuracy. This study may lead to the development of a new myoelectric control method for multi-DoF prostheses based on phantom movements of the amputee and kinematic data of the prosthesis.

[1]  Robert D. Lipschutz,et al.  Targeted reinnervation for enhanced prosthetic arm function in a woman with a proximal amputation: a case study , 2007, The Lancet.

[2]  Elaine Biddiss,et al.  Consumer design priorities for upper limb prosthetics , 2007, Disability and rehabilitation. Assistive technology.

[3]  K. Englehart,et al.  Resolving the Limb Position Effect in Myoelectric Pattern Recognition , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Levi J. Hargrove,et al.  Performance of pattern recognition myoelectric control using a generic electrode grid with Targeted Muscle Reinnervation patients , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Eladio Cardiel,et al.  Evaluation of suitability of a micro-processing unit of motion analysis for upper limb tracking. , 2016, Medical engineering & physics.

[6]  J. Wolpaw,et al.  Decoding flexion of individual fingers using electrocorticographic signals in humans , 2009, Journal of neural engineering.

[7]  Delphine Périé,et al.  Refinement of the upper limb joint kinematics and dynamics using a subject-specific closed-loop forearm model , 2015 .

[8]  Rajesh P. N. Rao,et al.  Control of a humanoid robot by a noninvasive brain–computer interface in humans , 2008, Journal of neural engineering.

[9]  K. Reilly,et al.  Persistent hand motor commands in the amputees' brain. , 2006, Brain : a journal of neurology.

[10]  Joris M. Lambrecht,et al.  Electromyogram-based neural network control of transhumeral prostheses. , 2011, Journal of rehabilitation research and development.

[11]  Erik Scheme,et al.  Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. , 2011, Journal of rehabilitation research and development.

[12]  M. Bransby-Zachary,et al.  Upper Limb Traumatic Amputees , 1997, Journal of hand surgery.

[13]  Levi J. Hargrove,et al.  Classification of Simultaneous Movements Using Surface EMG Pattern Recognition , 2013, IEEE Transactions on Biomedical Engineering.

[14]  R. A. R. C. Gopura,et al.  A review on hybrid myoelectric control systems for upper limb prosthesis , 2015, 2015 Moratuwa Engineering Research Conference (MERCon).

[15]  C. Nicol,et al.  Classification of Phantom Finger, Hand, Wrist, and Elbow Voluntary Gestures in Transhumeral Amputees With sEMG , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  Ernest Nlandu Kamavuako,et al.  Phantom movements from physiologically inappropriate muscles: A case study with a high transhumeral amputee , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[17]  T. Hortobágyi,et al.  Teager–Kaiser energy operator signal conditioning improves EMG onset detection , 2010, European Journal of Applied Physiology.

[18]  K. Englehart,et al.  Classification of the myoelectric signal using time-frequency based representations. , 1999, Medical engineering & physics.

[19]  Todd Kuiken,et al.  Pattern recognition control of multifunction myoelectric prostheses by patients with congenital transradial limb defects: a preliminary study , 2011, Prosthetics and orthotics international.

[20]  Huosheng Hu,et al.  Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb , 2008, IEEE Transactions on Biomedical Engineering.

[21]  Max Ortiz-Catalan,et al.  Real-Time and Simultaneous Control of Artificial Limbs Based on Pattern Recognition Algorithms , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

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

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

[25]  Øyvind Stavdahl,et al.  A multi-modal approach for hand motion classification using surface EMG and accelerometers , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  M. J. Highsmith,et al.  Differences in myoelectric and body-powered upper-limb prostheses: Systematic literature review. , 2015, Journal of rehabilitation research and development.

[27]  Levi J. Hargrove,et al.  A Comparison of Surface and Intramuscular Myoelectric Signal Classification , 2007, IEEE Transactions on Biomedical Engineering.

[28]  Linda Resnik,et al.  The DEKA Arm: Its features, functionality, and evolution during the Veterans Affairs Study to optimize the DEKA Arm , 2014, Prosthetics and orthotics international.

[29]  Ping Zhou,et al.  Teager–Kaiser Energy Operation of Surface EMG Improves Muscle Activity Onset Detection , 2007, Annals of Biomedical Engineering.

[30]  Roberto Merletti,et al.  Control of Powered Upper Limb Prostheses , 2004 .

[31]  K. Englehart,et al.  On the Suitability of Integrating Accelerometry Data with Electromyography Signals for Resolving the Effect of Changes in Limb Position during Dynamic Limb Movement , 2014 .

[32]  K. Reilly,et al.  Mapping phantom movement representations in the motor cortex of amputees. , 2006, Brain : a journal of neurology.

[33]  Thomas Seel,et al.  IMU-Based Joint Angle Measurement for Gait Analysis , 2014, Sensors.

[34]  Angkoon Phinyomark,et al.  EMG feature evaluation for improving myoelectric pattern recognition robustness , 2013, Expert Syst. Appl..

[35]  Barbara Caputo,et al.  Exploiting accelerometers to improve movement classification for prosthetics , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[36]  C. Nicol,et al.  Phantom hand and wrist movements in upper limb amputees are slow but naturally controlled movements , 2016, Neuroscience.

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

[38]  Robert D. Lipschutz,et al.  Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. , 2009, JAMA.

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

[40]  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.

[41]  F. K. Lam,et al.  Fuzzy EMG classification for prosthesis control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[42]  S. Bandinelli,et al.  Motor reorganization after upper limb amputation in man. A study with focal magnetic stimulation. , 1991, Brain : a journal of neurology.

[43]  L J Hargrove,et al.  Determining the Optimal Window Length for Pattern Recognition-Based Myoelectric Control: Balancing the Competing Effects of Classification Error and Controller Delay , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.