Estimation of excitatory drive from sparse motoneuron sampling

It is possible to replace amputated limbs with mechatronic prostheses, but their operation requires the user's intentions to be detected and converted into control signals sent to the actuators. Fortunately, the motoneurons (MNs) that controlled the amputated muscles remain intact and capable of generating electrical signals, but these signals are difficult to record. Even the latest microelectrode array technologies and targeted motor reinnervation (TMR) can provide only sparse sampling of the hundreds of motor units that comprise the motor pool for each muscle. Simple rectification and integration of such records is likely to produce noisy and delayed estimates of the actual intentions of the user. We have developed a novel algorithm for optimal estimation of motor pool excitation based on the recruitment and firing rates of a small number (2-10) of discriminated motor units. We first derived the motor estimation algorithm from normal patterns of modulated MN activity based on a previously published model of individual MN recruitment and asynchronous frequency modulation. The algorithm was then validated on a target motor reinnervation subject using intramuscular fine-wire recordings to obtain single motor units.

[1]  Eduardo Fernández,et al.  Long-term stimulation and recording with a penetrating microelectrode array in cat sciatic nerve , 2004, IEEE Transactions on Biomedical Engineering.

[2]  K. Søgaard,et al.  Control of the wrist joint in humans , 2000, European Journal of Applied Physiology.

[3]  Kazunori Yasuda,et al.  Behavior of single motor units of human tibialis anterior muscle during voluntary shortening contraction under constant load torque , 1985, Experimental Neurology.

[4]  R B Stein,et al.  The orderly recruitment of human motor units during voluntary isometric contractions , 1973, The Journal of physiology.

[5]  Todd A. Kuiken,et al.  Simulation of Intramuscular EMG Signals Detected Using Implantable Myoelectric Sensors (IMES) , 2006, IEEE Transactions on Biomedical Engineering.

[6]  Michael J. O'Donovan,et al.  Discharge patterns of hindlimb motoneurons during normal cat locomotion. , 1981, Science.

[7]  J. Borg,et al.  Axonal conduction velocity and voluntary discharge properties of individual short toe extensor motor units in man. , 1978, The Journal of physiology.

[8]  Michael J. O'Donovan,et al.  Cat hindlimb motoneurons during locomotion. II. Normal activity patterns. , 1987, Journal of neurophysiology.

[9]  D. Farina,et al.  Estimating motor unit discharge patterns from high-density surface electromyogram , 2009, Clinical Neurophysiology.

[10]  G. Loeb,et al.  Electromyography for Experimentalists , 1986 .

[11]  Michael J. O'Donovan,et al.  Cat Hindlimb Motoneurons During Locomotion , 2005 .

[12]  M. Hauschild,et al.  A Virtual Reality Environment for Designing and Fitting Neural Prosthetic Limbs , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  A. Monster,et al.  Isometric force production by motor units of extensor digitorum communis muscle in man. , 1977, Journal of neurophysiology.

[14]  D. Farina,et al.  Analysis of motor units with high-density surface electromyography. , 2008, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[15]  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.

[16]  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.