Offline and online myoelectric pattern recognition analysis and real-time control of a robotic hand after spinal cord injury

OBJECTIVE The objective of this study was to investigate the feasibility of applying myoelectric pattern recognition for controlling a robotic hand in individuals with spinal cord injury (SCI). APPROACH Surface electromyogram (sEMG) signals of six hand motion patterns were recorded from 12 subjects with SCI. Online and offline classification performance of two classifiers (Gaussian Naive Bayes classifier, GNB, and support vector machine, SVM) were investigated. An exoskeleton hand was then controlled in real-time using the classification results. The control accuracy and its correlation with function assessments were investigated. MAIN RESULTS Average offline classification accuracy of all tested SCI subjects was (73.6  ±  14.0)% for GNB and (77.6  ±  11.6)% for SVM, respectively. Average online classification accuracy was significantly lower, (64.3  ±  15.0)% for GNB and (70.2  ±  13.2)% for SVM. Average control accuracy of (81.0  ±  16.3)% was achieved in real-time control of the robotic hand using myoelectric pattern recognition. Correlation between control accuracy and grip/pinch force was observed. SIGNIFICANCE The results show that it is feasible to extract hand motion intent from individuals with SCI and control a robotic hand device using myoelectric pattern recognition. The performance of real-time control can be predicted based on functional assessments.

[1]  Ping Zhou,et al.  Myoelectric Pattern Recognition for Controlling a Robotic Hand: A Feasibility Study in Stroke , 2019, IEEE Transactions on Biomedical Engineering.

[2]  Huosheng Hu,et al.  Myoelectric control systems - A survey , 2007, Biomed. Signal Process. Control..

[3]  Kyle D. Fitle,et al.  Robot-Assisted Training of Arm and Hand Movement Shows Functional Improvements for Incomplete Cervical Spinal Cord Injury , 2017, American journal of physical medicine & rehabilitation.

[4]  Xu Zhang,et al.  Multiple Hand Gesture Recognition Based on Surface EMG Signal , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[5]  Ching-Yi Wu,et al.  Kinematic Manifestation of Arm-Trunk Performance during Symmetric Bilateral Reaching After Stroke: Within vs. Beyond Arm’s Length , 2017, American journal of physical medicine & rehabilitation.

[6]  Ping Zhou,et al.  Robotic Hand–Assisted Training for Spinal Cord Injury Driven by Myoelectric Pattern Recognition: A Case Report , 2017, American journal of physical medicine & rehabilitation.

[7]  Gamini Dissanayake,et al.  Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals , 2012, Expert Syst. Appl..

[8]  Miguel Angel Mañanas,et al.  Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury , 2016, Journal of NeuroEngineering and Rehabilitation.

[9]  Fuchun Sun,et al.  sEMG-Based Joint Force Control for an Upper-Limb Power-Assist Exoskeleton Robot , 2014, IEEE Journal of Biomedical and Health Informatics.

[10]  Christian Antfolk,et al.  Using EMG for Real-time Prediction of Joint Angles to Control a Prosthetic Hand Equipped with a Sensory Feedback System , 2010 .

[11]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[12]  T. Platz,et al.  Device-Training for Individuals with Thoracic and Lumbar Spinal Cord Injury Using a Powered Exoskeleton for Technically Assisted Mobility: Achievements and User Satisfaction , 2016, BioMed research international.

[13]  Ping Zhou,et al.  Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition , 2017, Int. J. Neural Syst..

[14]  R. Hughes,et al.  Electromyography-Controlled Exoskeletal Upper-Limb–Powered Orthosis for Exercise Training After Stroke , 2007, American journal of physical medicine & rehabilitation.

[15]  Todd A. Kuiken,et al.  The Effect of ECG Interference on Pattern-Recognition-Based Myoelectric Control for Targeted Muscle Reinnervated Patients , 2009, IEEE Transactions on Biomedical Engineering.

[16]  M. Popovic,et al.  The Graded Redefined Assessment of Strength Sensibility and Prehension: reliability and validity. , 2012, Journal of neurotrauma.

[17]  Rong Song,et al.  A Comparison Between Electromyography-Driven Robot and Passive Motion Device on Wrist Rehabilitation for Chronic Stroke , 2009, Neurorehabilitation and neural repair.

[18]  Guido Bugmann,et al.  Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography , 2013, IEEE Journal of Biomedical and Health Informatics.

[19]  C. Burgar,et al.  Quantification of force abnormalities during passive and active-assisted upper-limb reaching movements in post-stroke hemiparesis , 1999, IEEE Transactions on Biomedical Engineering.

[20]  Brian Byunghyun Kang,et al.  Exo-Glove: A Wearable Robot for the Hand with a Soft Tendon Routing System , 2015, IEEE Robotics & Automation Magazine.

[21]  W. Rymer,et al.  The effect of involuntary motor activity on myoelectric pattern recognition: a case study with chronic stroke patients , 2013, Journal of neural engineering.

[22]  Erik J. Scheme,et al.  Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions , 2011, IEEE Transactions on Biomedical Engineering.

[23]  Ying Wu,et al.  Modeling the constraints of human hand motion , 2000, Proceedings Workshop on Human Motion.

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

[25]  Nicola Vitiello,et al.  Intention-Based EMG Control for Powered Exoskeletons , 2012, IEEE Transactions on Biomedical Engineering.

[26]  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.

[27]  Tomohiro Shibata,et al.  Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model , 2014, Journal of NeuroEngineering and Rehabilitation.

[28]  Ping Zhou,et al.  A Novel Myoelectric Pattern Recognition Strategy for Hand Function Restoration After Incomplete Cervical Spinal Cord Injury , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[29]  Kevin C. Galloway,et al.  Assisting hand function after spinal cord injury with a fabric-based soft robotic glove , 2018, Journal of NeuroEngineering and Rehabilitation.

[30]  Yoshiaki Hayashi,et al.  An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[31]  Xiang Chen,et al.  A prototype of gesture-based interface , 2011, Mobile HCI.

[32]  P. Schwenkreis,et al.  Voluntary driven exoskeleton as a new tool for rehabilitation in chronic spinal cord injury: a pilot study. , 2014, The spine journal : official journal of the North American Spine Society.

[33]  Kongqiao Wang,et al.  A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[34]  D. Reinkensmeyer,et al.  Review of control strategies for robotic movement training after neurologic injury , 2009, Journal of NeuroEngineering and Rehabilitation.

[35]  Arto Visala,et al.  urrent state of digital signal processing in myoelectric interfaces and elated applications , 2015 .

[36]  Maura Casadio,et al.  Reorganization of finger coordination patterns during adaptation to rotation and scaling of a newly learned sensorimotor transformation. , 2011, Journal of neurophysiology.

[37]  Elsa Andrea Kirchner,et al.  Exoskeleton Technology in Rehabilitation: Towards an EMG-Based Orthosis System for Upper Limb Neuromotor Rehabilitation , 2013, J. Robotics.

[38]  Vicky Chan,et al.  Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial , 2017, Neurorehabilitation and neural repair.

[39]  Mamun Bin Ibne Reaz,et al.  Surface Electromyography Signal Processing and Classification Techniques , 2013, Sensors.

[40]  Guanglin Li,et al.  EMG feature assessment for myoelectric pattern recognition and channel selection: a study with incomplete spinal cord injury. , 2014, Medical engineering & physics.

[41]  Antonio Frisoli,et al.  An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation , 2015, IEEE Transactions on Haptics.

[42]  Qiang Li,et al.  A Hand Gesture Recognition Framework and Wearable Gesture-Based Interaction Prototype for Mobile Devices , 2014, IEEE Transactions on Human-Machine Systems.

[43]  Wei Zhou,et al.  Myoelectrically controlled wrist robot for stroke rehabilitation , 2013, Journal of NeuroEngineering and Rehabilitation.

[44]  Max Ortiz-Catalan,et al.  BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms , 2013, Source Code for Biology and Medicine.

[45]  Xiaolin Liu,et al.  Contributions of online visual feedback to the learning and generalization of novel finger coordination patterns. , 2008, Journal of neurophysiology.

[46]  E. A. Susanto,et al.  The effects of post-stroke upper-limb training with an electromyography (EMG)-driven hand robot. , 2013, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[47]  D.S. Andreasen,et al.  Exoskeleton with EMG based active assistance for rehabilitation , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[48]  Alberto Borboni,et al.  Gloreha - Hand robotic rehabilitation: design, mechanical model and experiments , 2016 .

[49]  Marcia Kilchenman O'Malley,et al.  Design and validation of the RiceWrist-S exoskeleton for robotic rehabilitation after incomplete spinal cord injury , 2014, Robotica.

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

[51]  Carlo Menon,et al.  Surface EMG pattern recognition for real-time control of a wrist exoskeleton , 2010, Biomedical engineering online.

[52]  N. Shoylev,et al.  Neural Networks for Online Classification of Hand and Finger Movements Using Surface EMG signals , 2006, 2006 8th Seminar on Neural Network Applications in Electrical Engineering.

[53]  Ping Zhou,et al.  Decoding a new neural machine interface for control of artificial limbs. , 2007, Journal of neurophysiology.