Dimensionality effect of myoelectric-controlled interface on the coordination of agonist and antagonist muscles during voluntary isometric elbow flexion and extension

Abstract This study aimed to investigate the dimensionality effect of myoelectric-controlled interface (MCI) on the coordination of agonist and antagonist muscles during voluntary isometric elbow flexion and extension. Eighteen healthy subjects were recruited to control a controllable cursor to track a target cursor by real-time modulating the biceps and triceps activities within one-dimensional and two-dimensional MCIs. Electromyographic (EMG) signals were collected to calculate the normalized muscle activation, while the slope of the best-fitting linear relationship between the normalized agonist and antagonist activations was used to quantify the muscle co-activation. The tracking error and the normalized net torque of the elbow joint were also calculated. Results showed that no significant difference was found in the tracking error between one-dimensional and two-dimensional MCIs. The normalized antagonist activation, the muscle co-activation and the normalized net torque were significantly lower within two-dimensional MCI than within one-dimensional MCI. In addition, significant decrease in the normalized agonist activation was also found during elbow extension. These results implied that within two-dimensional MCI, subjects were able to modulate the coordination of agonist and antagonist precisely by inhibiting unnecessary muscle activities. Therefore, two-dimensional MCI might be applied as a rehabilitation tool aiming at fine control of abnormal muscle coordination.

[1]  W. Rymer,et al.  Abnormal muscle coactivation patterns during isometric torque generation at the elbow and shoulder in hemiparetic subjects. , 1995, Brain : a journal of neurology.

[2]  J. Chae,et al.  Muscle Weakness and Cocontraction in Upper Limb Hemiparesis: Relationship to Motor Impairment and Physical Disability , 2002, Neurorehabilitation and neural repair.

[3]  Judith E. Deutsch,et al.  High Metabolic Cost and Low Energy Expenditure for Typical Motor Activities Among Individuals in the Chronic Phase After Stroke , 2014, Journal of neurologic physical therapy : JNPT.

[4]  J. T. Mordkoff,et al.  Dividing attention between color and shape revisited: redundant targets coactivate only when parts of the same perceptual object , 2011, Attention, perception & psychophysics.

[5]  G. Gottlieb,et al.  Organizing principles for single-joint movements. IV. Implications for isometric contractions. , 1990, Journal of neurophysiology.

[6]  Andrew Jackson,et al.  Learning a Novel Myoelectric-Controlled Interface Task , 2008, Journal of neurophysiology.

[7]  Colleen G. Canning,et al.  Abnormal muscle activation characteristics associated with loss of dexterity after stroke , 2000, Journal of the Neurological Sciences.

[8]  C. D. De Luca,et al.  Voluntary control of motor units in human antagonist muscles: coactivation and reciprocal activation. , 1987, Journal of neurophysiology.

[9]  R. D'ambrosia,et al.  Muscular coactivation , 1988, The American journal of sports medicine.

[10]  J. Nielsen,et al.  Segmental reflexes and ankle joint stiffness during co-contraction of antagonistic ankle muscles in man , 1994, Experimental Brain Research.

[11]  M Solomonow,et al.  Muscular co-contraction and control of knee stability. , 1991, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[12]  Paul L Gribble,et al.  Role of cocontraction in arm movement accuracy. , 2003, Journal of neurophysiology.

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

[14]  D. Gagnon,et al.  The comparison of trunk muscles EMG activation between subjects with and without chronic low back pain during flexion-extension and lateral bending tasks. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[15]  Pascal Madeleine,et al.  Biofeedback effectiveness to reduce upper limb muscle activity during computer work is muscle specific and time pressure dependent. , 2011, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[16]  Helen J. Huang,et al.  Reduction of Metabolic Cost during Motor Learning of Arm Reaching Dynamics , 2012, The Journal of Neuroscience.

[17]  Charles R. Crowell,et al.  Relative efficacy of various strategies for visual feedback in standing balance activities , 2013, Experimental Brain Research.

[18]  D. Farina,et al.  Surface Electromyography for Noninvasive Characterization of Muscle , 2001, Exercise and sport sciences reviews.

[19]  Daniel P. Ferris,et al.  Proportional myoelectric control of a virtual object to investigate human efferent control , 2004, Experimental Brain Research.

[20]  Denis Mottet,et al.  The role of cocontraction in the impairment of movement accuracy with fatigue , 2008, Experimental Brain Research.

[21]  J. Nielsen,et al.  The regulation of presynaptic inhibition during co‐contraction of antagonistic muscles in man. , 1993, The Journal of physiology.

[22]  Francesco Felici,et al.  Tennis players show a lower coactivation of the elbow antagonist muscles during isokinetic exercises. , 2008, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[23]  M. O'Malley,et al.  Effect of elbow joint angle on force-EMG relationships in human elbow flexor and extensor muscles. , 2008, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[24]  Dario Farina,et al.  The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[25]  Marc W Slutzky,et al.  Reducing Abnormal Muscle Coactivation After Stroke Using a Myoelectric-Computer Interface , 2014, Neurorehabilitation and neural repair.

[26]  Thomas Graven-Nielsen,et al.  Modulation of motor variability related to experimental muscle pain during elbow-flexion contractions. , 2015, Human movement science.

[27]  G. Zorzi,et al.  EMG-Based Visual-Haptic Biofeedback: A Tool to Improve Motor Control in Children With Primary Dystonia , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Scott J. Young,et al.  Visual Feedback Reduces Co-contraction in Children With Dystonia , 2011, Journal of child neurology.

[29]  Hiroyuki Kambara,et al.  Control of a Brick-Breaking Game Using Electromyogram , 2014 .

[30]  D. Winter,et al.  Crosstalk in surface electromyography: Theoretical and practical estimates. , 1994, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

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

[32]  Daniel W. Stashuk,et al.  Detection of motor unit action potentials with surface electrodes: influence of electrode size and spacing , 1992, Biological Cybernetics.