Towards the development of a robust model for estimating wrist torque at different wrist angles

Several robotic devices have been developed to assist patients regain their autonomy. These robotic devices augment forces exerted by the device wearer. A majority of these devices are controlled by surface electromyography (SEMG) signals acquired from wearer's muscles. Several regression models are available for estimation of wrist torques using SEMG signals. Common issues related to torque estimation models are degradation of model accuracy when the limb posture is altered. This degradation renders these models unsuitable for practical applications. For this reason, a model is sought that is unaffected by varying arm postures and joint angles. Obtained results for overcoming this problem are presented.

[1]  M. Ferrarin,et al.  EMG signals detection and processing for on-line control of functional electrical stimulation. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[2]  Carlo Menon,et al.  Towards the development of a wearable rehabilitation device for stroke survivors , 2009, 2009 IEEE Toronto International Conference Science and Technology for Humanity (TIC-STH).

[3]  S. Chatterjee,et al.  Influential Observations, High Leverage Points, and Outliers in Linear Regression , 1986 .

[4]  M. Hahn.  Feasibility of estimating isokinetic knee torque using a neural network model. , 2007, Journal of biomechanics.

[5]  G. Gini,et al.  An EMG-controlled exoskeleton for hand rehabilitation , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[6]  J. Morley,et al.  The top 10 hot topics in aging. , 2004, The journals of gerontology. Series A, Biological sciences and medical sciences.

[7]  Othman Omran Khalifa,et al.  EMG signal classification for human computer interaction: a review , 2009 .

[8]  Toshio Tsuji,et al.  A human-assisting manipulator teleoperated by EMG signals and arm motions , 2003, IEEE Trans. Robotics Autom..

[9]  Nicole M Grosland,et al.  Impact of impaired wrist motion on hand and upper-extremity performance(1). , 2003, The Journal of hand surgery.

[10]  D F Stegeman,et al.  Recent progress in the diagnostic use of surface EMG for neurological diseases. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

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

[12]  Yoky Matsuoka,et al.  Comparison of control strategies for an EMG controlled orthotic exoskeleton for the hand , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[13]  H. Kawasaki,et al.  Development of a Hand Motion Assist Robot for Rehabilitation Therapy by Patient Self-Motion Control , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[14]  A.D.C. Chan,et al.  Examining the adverse effects of limb position on pattern recognition based myoelectric control , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[15]  Richard F. Gunst,et al.  Applied Regression Analysis , 1999, Technometrics.

[16]  S. Wolf,et al.  A pneumatic muscle hand therapy device , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Suncheol Kwon,et al.  Real-time estimation of thumb-tip forces using surface electromyogram for a novel human-machine interface , 2010, 2010 IEEE International Conference on Robotics and Automation.

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

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

[20]  L. Maier-Hein,et al.  Session independent non-audible speech recognition using surface electromyography , 2005, IEEE Workshop on Automatic Speech Recognition and Understanding, 2005..

[21]  Joel C. Perry,et al.  Real-Time Myoprocessors for a Neural Controlled Powered Exoskeleton Arm , 2006, IEEE Transactions on Biomedical Engineering.

[22]  Yoky Matsuoka,et al.  An EMG-Controlled Hand Exoskeleton for Natural Pinching , 2004, J. Robotics Mechatronics.

[23]  D. Erol,et al.  Coordinated Control of Assistive Robotic Devices for Activities of Daily Living Tasks , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  R. Hughes,et al.  Evaluation of muscle force prediction models of the lumbar trunk using surface electromyography , 1994, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[25]  Yongji Wang,et al.  A wearable device for repetitive hand therapy , 2008, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[26]  J. Schaechter.  Motor rehabilitation and brain plasticity after hemiparetic stroke , 2004, Progress in Neurobiology.

[27]  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.

[28]  Y. Y. Huang,et al.  Objective and quantitative assessment methodology of hand functions for rehabilitation , 2009, 2008 IEEE International Conference on Robotics and Biomimetics.

[29]  L. Der-Yeghiaian,et al.  Robot-based hand motor therapy after stroke. , 2007, Brain : a journal of neurology.

[30]  Ricardo Carelli,et al.  Human-machine interfaces based on EMG and EEG applied to robotic systems , 2008, Journal of NeuroEngineering and Rehabilitation.

[31]  Ian E. Brown,et al.  Virtual muscle: a computational approach to understanding the effects of muscle properties on motor control , 2000, Journal of Neuroscience Methods.

[32]  Yasuharu Koike,et al.  Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model , 1995, Biological Cybernetics.

[33]  Junuk Chu,et al.  Wearable EMG-based HCI for Electric-Powered Wheelchair Users with Motor Disabilities , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[34]  H. Krebs,et al.  Effects of Robot-Assisted Therapy on Upper Limb Recovery After Stroke: A Systematic Review , 2008, Neurorehabilitation and neural repair.

[35]  N. Draper,et al.  Applied Regression Analysis: Draper/Applied Regression Analysis , 1998 .

[36]  Yasuhisa Hasegawa,et al.  Five-fingered assistive hand with mechanical compliance of human finger , 2008, 2008 IEEE International Conference on Robotics and Automation.