Control of Robotic Assistance Using Poststroke Residual Voluntary Effort

Poststroke hemiparesis limits the ability to reach, in part due to involuntary muscle co-activation (synergies). Robotic approaches are being developed for both therapeutic benefit and continuous assistance during activities of daily living. Robotic assistance may enable participants to exert less effort, thereby reducing expression of the abnormal co-activation patterns, which could allow participants to reach further. This study evaluated how well participants could perform a reaching task with robotic assistance that was either provided independent of effort in the vertical direction or in the sagittal plane in proportion to voluntary effort estimated from electromyograms (EMG) on the affected side. Participants who could not reach targets without assistance were enabled to reach further with assistance. Constant anti-gravity force assistance that was independent of voluntary effort did not reduce the quality of reach and enabled participants to exert less effort while maintaining different target locations. Force assistance that was proportional to voluntary effort on the affected side enabled participants to exert less effort and could be controlled to successfully reach targets, but participants had increased difficulty maintaining a stable position. These results suggest that residual effort on the affected side can produce an effective command signal for poststroke assistive devices.

[1]  Jacob Rosen,et al.  Admittance control of an upper limb exoskeleton - Reduction of energy exchange , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  D. Reinkensmeyer,et al.  Directional control of reaching is preserved following mild/moderate stroke and stochastically constrained following severe stroke , 2002, Experimental Brain Research.

[3]  Julius P. A. Dewald,et al.  Involuntary paretic wrist/finger flexion forces and EMG increase with shoulder abduction load in individuals with chronic stroke , 2012, Clinical Neurophysiology.

[4]  Aimee P. Reiss,et al.  Constraint-Induced Movement Therapy (CIMT): Current Perspectives and Future Directions , 2012, Stroke research and treatment.

[5]  Ping Zhou,et al.  Real time ECG artifact removal for myoelectric prosthesis control , 2007, Physiological measurement.

[6]  T. Twitchell,et al.  Sensory factors in purposive movement. , 1954, Journal of neurophysiology.

[7]  R. Hart,et al.  Intramuscular Hand Neuroprosthesis for Chronic Stroke Survivors , 2003, Neurorehabilitation and neural repair.

[8]  P.E. Crago,et al.  Functional restoration of elbow extension after spinal-cord injury using a neural network-based synergistic FES controller , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Young-Hui Chang,et al.  Autogenic EMG-controlled functional electrical stimulation for ankle dorsiflexion control , 2010, Journal of Neuroscience Methods.

[10]  G. Cheron,et al.  A dynamic neural network identification of electromyography and arm trajectory relationship during complex movements , 1996, IEEE Transactions on Biomedical Engineering.

[11]  J. Giuffrida,et al.  Upper-Extremity Stroke Therapy Task Discrimination Using Motion Sensors and Electromyography , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  J. Patton,et al.  Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors , 2005, Experimental Brain Research.

[14]  D G Kamper,et al.  Quantitative features of the stretch response of extrinsic finger muscles in hemiparetic stroke , 2000, Muscle & nerve.

[15]  J. Chae,et al.  Delay in initiation and termination of muscle contraction, motor impairment, and physical disability in upper limb hemiparesis , 2002, Muscle & nerve.

[16]  Wen Yu,et al.  Neural PID Control of Robot Manipulators With Application to an Upper Limb Exoskeleton , 2013, IEEE Transactions on Cybernetics.

[17]  L. Connell,et al.  Somatosensory impairment after stroke: frequency of different deficits and their recovery , 2008, Clinical rehabilitation.

[18]  Mary Y. Harley,et al.  Implanted neuroprosthesis for assisting arm and hand function after stroke: a case study. , 2012, Journal of rehabilitation research and development.

[19]  D. Howard,et al.  Artificial Neural Network Prediction Using Accelerometers to Control Upper Limb FES During Reaching and Grasping Following Stroke , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Jiping He,et al.  Feasibility studies of robot-assisted stroke rehabilitation at clinic and home settings using RUPERT , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[21]  J. L. Patton,et al.  Arm control recovery enhanced by error augmentation , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[22]  Le Li,et al.  Assistive Control System Using Continuous Myoelectric Signal in Robot-Aided Arm Training for Patients After Stroke , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[24]  Nathaniel Makowski,et al.  Interaction of poststroke voluntary effort and functional neuromuscular electrical stimulation. , 2013, Journal of rehabilitation research and development.

[25]  Lee E. Miller,et al.  Continuous movement decoding using a target-dependent model with EMG inputs , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  R. Kirsch,et al.  EMG-based prediction of shoulder and elbow kinematics in able-bodied and spinal cord injured individuals. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[27]  Nicola Vitiello,et al.  Proportional EMG control for upper-limb powered exoskeletons , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  Thierry Keller,et al.  Overcoming abnormal joint torque patterns in paretic upper extremities using triceps stimulation. , 2005, Artificial organs.

[29]  Valerie Hill,et al.  Portable upper extremity robotics is as efficacious as upper extremity rehabilitative therapy: a randomized controlled pilot trial , 2013, Clinical rehabilitation.

[30]  David J. Reinkensmeyer,et al.  Hemiparetic stroke impairs anticipatory control of arm movement , 2003, Experimental Brain Research.

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

[32]  J. Dewald,et al.  Shoulder abduction-induced reductions in reaching work area following hemiparetic stroke: neuroscientific implications , 2007, Experimental Brain Research.

[33]  J. Krakauer,et al.  A computational neuroanatomy for motor control , 2008, Experimental Brain Research.

[34]  Nathaniel S. Makowski,et al.  Contralaterally controlled functional electrical stimulation for stroke rehabilitation , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[35]  P. Crago,et al.  Effects of voluntary force generation on the elastic components of endpoint stiffness , 2001, Experimental Brain Research.

[36]  Ming-Shaung Ju,et al.  Improving elbow torque output of stroke patients with assistive torque controlled by EMG signals. , 2003, Journal of biomechanical engineering.

[37]  Jung Kim,et al.  Estimation of elbow flexion force during isometric muscle contraction from mechanomyography and electromyography , 2010, Medical & Biological Engineering & Computing.

[38]  Jules P. A. Dewald,et al.  Impairment-Based 3-D Robotic Intervention Improves Upper Extremity Work Area in Chronic Stroke: Targeting Abnormal Joint Torque Coupling With Progressive Shoulder Abduction Loading , 2009, IEEE Transactions on Robotics.

[39]  I W Hunter,et al.  Human ankle joint stiffness over the full range of muscle activation levels. , 1988, Journal of biomechanics.

[40]  J. Giuffrida,et al.  Synergistic Neural Network Control of FES Elbow Extension After Spinal Cord Injury Using EMG , 2004 .

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

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

[43]  J. Dewald,et al.  Abnormal joint torque patterns in the paretic upper limb of subjects with hemiparesis , 2001, Muscle & nerve.

[44]  Ali Ameri,et al.  A comparison between force and position control strategies in myoelectric prostheses , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[45]  Rong Song,et al.  Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations , 2005, Medical and Biological Engineering and Computing.

[46]  Nathaniel S. Makowski,et al.  Variations in neuromuscular electrical stimulation's ability to increase reach and hand opening during voluntary effort after stroke , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.