Evaluation of EMG, force and joystick as control interfaces for active arm supports

BackgroundThe performance capabilities and limitations of control interfaces for the operation of active movement-assistive devices remain unclear. Selecting an optimal interface for an application requires a thorough understanding of the performance of multiple control interfaces.MethodsIn this study the performance of EMG-, force- and joystick-based control interfaces were assessed in healthy volunteers with a screen-based one-dimensional position-tracking task. The participants had to track a target that was moving according to a multisine signal with a bandwidth of 3 Hz. The velocity of the cursor was proportional to the interface signal. The performance of the control interfaces were evaluated in terms of tracking error, gain margin crossover frequency, information transmission rate and effort.ResultsNone of the evaluated interfaces was superior in all four performance descriptors. The EMG-based interface was superior in tracking error and gain margin crossover frequency compared to the force- and the joystick-based interfaces. The force-based interface provided higher information transmission rate and lower effort than the EMG-based interface. The joystick-based interface did not present any significant difference with the force-based interface for any of the four performance descriptors. We found that significant differences in terms of tracking error and information transmission rate were present beyond 0.9 and 1.4 Hz respectively.ConclusionsDespite the fact that the EMG-based interface is far from the natural way of interacting with the environment, while the force-based interface is closer, the EMG-based interface presented very similar and for some descriptors even a better performance than the force-based interface for frequencies below 1.4 Hz. The classical joystick presented a similar performance to the force-based interface and holds the advantage of being a well established interface for the control of many assistive devices. From these findings we concluded that all the control interfaces considered in this study can be regarded as a candidate interface for the control of an active arm support.

[1]  Rory A. Cooper,et al.  Performance assessment of a pushrim-activated power-assisted wheelchair control system , 2002, IEEE Trans. Control. Syst. Technol..

[2]  Childress Ds,et al.  Design and evaluation of a prosthesis control system based on the concept of extended physiological proprioception. , 1984 .

[3]  J.C. Perry,et al.  Upper-Limb Powered Exoskeleton Design , 2007, IEEE/ASME Transactions on Mechatronics.

[4]  Silvestro Micera,et al.  A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems , 2005, Journal of the peripheral nervous system : JPNS.

[5]  J. Jansen,et al.  Review Overview of Actuated Arm Support Systems and Their Applications , 2013 .

[6]  G R Johnson,et al.  The design of a five-degree-of-freedom powered orthosis for the upper limb , 2001, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[7]  J. Schoukens,et al.  Parametric identification of transfer functions in the frequency domain-a survey , 1994, IEEE Trans. Autom. Control..

[8]  B Hudgins,et al.  Myoelectric signal processing for control of powered limb prostheses. , 2006, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[9]  J. I. Elkind,et al.  Transmission of Information in Simple Manual Control Systems , 1961 .

[10]  R Jiménez-Fabián,et al.  Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. , 2012, Medical engineering & physics.

[11]  Todd A Kuiken,et al.  Comparison of electromyography and force as interfaces for prosthetic control. , 2011, Journal of rehabilitation research and development.

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

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

[14]  Kazuo Kiguchi,et al.  Electromyography (EMG)-signal based fuzzy-neuro control of a 3 degrees of freedom (3DOF) exoskeleton robot for human upper-limb motion assist , 2009 .

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

[16]  Kevin Englehart,et al.  Evaluation of shoulder complex motion-based input strategies for endpoint prosthetic-limb control using dual-task paradigm. , 2011, Journal of rehabilitation research and development.

[17]  Ganwen Zeng,et al.  An overview of robot force control , 1997, Robotica.

[18]  D. Childress,et al.  Design and evaluation of a prosthesis control system based on the concept of extended physiological proprioception. , 1984, Journal of rehabilitation research and development.

[19]  David Howard,et al.  A comparative evaluation of sonomyography, electromyography, force, and wrist angle in a discrete tracking task. , 2011, Ultrasound in medicine & biology.

[20]  Mark R. Pitkin Prosthetic restoration and rehabilitation of the upper and lower extremity , 2015 .

[21]  Sheng Quan Xie,et al.  Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. , 2012, Medical engineering & physics.

[22]  R Seliktar,et al.  Towards the control of a powered orthosis for people with muscular dystrophy , 2001, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[23]  Sunil Agrawal,et al.  Series elastic actuator control of a powered exoskeleton , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[25]  Dudley S. Childress,et al.  On information transmission in human-machine systems: channel capacity and optimal filtering , 1990, IEEE Trans. Syst. Man Cybern..

[26]  H. Hermens,et al.  European recommendations for surface electromyography: Results of the SENIAM Project , 1999 .

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

[28]  Gert Jan Gelderblom,et al.  Dynamic arm supports: Overview and categorization of dynamic arm supports for people with decreased arm function , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[29]  Francois Routhier,et al.  Evaluation of the JACO robotic arm: Clinico-economic study for powered wheelchair users with upper-extremity disabilities , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[30]  Khairul Anam,et al.  Active Exoskeleton Control Systems: State of the Art , 2012 .

[31]  H.J.A. Stuyt,et al.  Cost-savings and economic benefits due to the assistive robotic manipulator (ARM) , 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

[32]  T. Kuiken,et al.  Neural Interfaces for Control of Upper Limb Prostheses: The State of the Art and Future Possibilities , 2011, PM & R : the journal of injury, function, and rehabilitation.

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

[34]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[35]  Frans C. T. van der Helm,et al.  Identification of intrinsic and reflexive components of human arm dynamics during postural control , 2002, Journal of Neuroscience Methods.

[36]  O. Stavdahl,et al.  Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[37]  Rory A Cooper,et al.  Joystick control for powered mobility: current state of technology and future directions. , 2010, Physical medicine and rehabilitation clinics of North America.

[38]  I. Stokes,et al.  Relationships of EMG to effort in the trunk under isometric conditions: force-increasing and decreasing effects and temporal delays. , 2005, Clinical biomechanics.

[39]  Rik Pintelon,et al.  System Identification: A Frequency Domain Approach , 2012 .

[40]  Duane T. McRuer,et al.  A Review of Quasi-Linear Pilot Models , 1967 .

[41]  K. Abbruzzese,et al.  An innovative design for an Assistive Arm Orthosis for stroke and muscle dystrophy , 2011, 2011 IEEE 37th Annual Northeast Bioengineering Conference (NEBEC).

[42]  Frans C. T. van der Helm,et al.  Design of a torque-controlled manipulator to analyse the admittance of the wrist joint , 2006, Journal of Neuroscience Methods.

[43]  S. Gandevia,et al.  The effect of sustained low‐intensity contractions on supraspinal fatigue in human elbow flexor muscles , 2006, The Journal of physiology.

[44]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[45]  Robert D. Lipschutz,et al.  Use of two-axis joystick for control of externally powered shoulder disarticulation prostheses. , 2011, Journal of rehabilitation research and development.