A Decision-Based Velocity Ramp for Minimizing the Effect of Misclassifications During Real-Time Pattern Recognition Control

Real-time pattern recognition control is frequently affected by misclassifications. This study investigated the use of a decision-based velocity ramp that attenuated movement speed after a change in classifier decision. The goal was to improve prosthesis positioning by minimizing the effect of unintended movements. Nonamputee and amputee subjects controlled a prosthesis in real time using pattern recognition. While performing a target achievement test in a virtual environment, subjects had a significantly higher completion rate (p <; 0.05) and a more direct path (p <; 0.05) to the target with the velocity ramp than without it. Using a physical prosthesis, subjects stacked a greater average number of 1-in cubes (p <; 0.05) in 3 min with the velocity ramp than without it (76% more blocks for nonamputees; 89% more blocks for amputees). Real-time control using the velocity ramp also showed significant performance improvements above using majority vote. Eighty-three percent of subjects preferred to control the prosthesis using the velocity ramp. These results suggest that using a decision-based velocity ramp with pattern recognition may improve user performance. Since the velocity ramp is a postprocessing step, it has the potential to be used with a variety of classifiers for many applications.

[1]  N.V. Thakor,et al.  Towards the Control of Individual Fingers of a Prosthetic Hand Using Surface EMG Signals , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Ian D. Walker,et al.  Myoelectric teleoperation of a complex robotic hand , 1996, IEEE Trans. Robotics Autom..

[3]  Todd A Kuiken,et al.  Target Achievement Control Test: evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses. , 2011, Journal of rehabilitation research and development.

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

[5]  Richard F. Weir,et al.  ANALYSIS WINDOW INDUCED CONTROLLER DELAY FOR MULTIFUNCTIONAL PROSTHESES , 2008 .

[6]  R.F. Weir,et al.  The Optimal Controller Delay for Myoelectric Prostheses , 2007, IEEE Transactions on Neural Systems and Rehabilitation 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]  D Graupe,et al.  Multifunctional prosthesis and orthosis control via microcomputer identification of temporal pattern differences in single-site myoelectric signals. , 1982, Journal of biomedical engineering.

[9]  M. Swiontkowski.  Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms , 2010 .

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

[11]  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.

[12]  Robert D. Lipschutz,et al.  The use of targeted muscle reinnervation for improved myoelectric prosthesis control in a bilateral shoulder disarticulation amputee , 2004, Prosthetics and orthotics international.

[13]  B. Hudgins,et al.  REAL-TIME MYOELECTRIC CONTROL IN A VIRTUAL ENVIRONMENT TO RELATE USABILITY VS. ACCURACY , 2005 .

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

[15]  L. E. Peppard,et al.  Feature-based classification of myoelectric signals using artificial neural networks , 1998, Medical and Biological Engineering and Computing.

[16]  H. H. Sears,et al.  PROPORTIONAL MYOELECTRIC HAND CONTROL: AN EVALUATION , 1991, American journal of physical medicine & rehabilitation.

[17]  T. Kuiken,et al.  Control of a six degree of freedom prosthetic arm after targeted muscle reinnervation surgery. , 2008, Archives of physical medicine and rehabilitation.

[18]  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.

[19]  Adrian D. C. Chan,et al.  Continuous myoelectric control for powered prostheses using hidden Markov models , 2005, IEEE Transactions on Biomedical Engineering.

[20]  Blair A. Lock,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.

[21]  R.F. Kirsch,et al.  Evaluation of Head Orientation and Neck Muscle EMG Signals as Command Inputs to a Human–Computer Interface for Individuals With High Tetraplegia , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Toshio Tsuji,et al.  EMG-based human-robot interface for rehabilitation aid , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[23]  He Huang,et al.  An Analysis of EMG Electrode Configuration for Targeted Muscle Reinnervation Based Neural Machine Interface , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[25]  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.

[26]  T. Kuiken.  Targeted reinnervation for improved prosthetic function. , 2006, Physical Medicine and Rehabilitation Clinics of North America.

[27]  K.B. Englehart,et al.  Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

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

[30]  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.