Roger V. Gonzalez

A real-time EMG-driven virtual arm

An EMG-driven virtual arm is being developed in our laboratories for the purposes of studying neuromuscular control of arm movements. The virtual arm incorporates the major muscles spanning the elbow joint and is used to estimate tension developed by individual muscles based on recorded electromyograms (EMGs). It is able to estimate joint moments and the corresponding virtual movements, which are displayed in real-time on a computer screen. In addition, the virtual arm offers artificial control over a variety of physiological and environmental conditions. The virtual arm can be used to examine how the neuromuscular system compensates for the partial or total loss of a muscles ability to generate force as might result from trauma or pathology. The purpose of this paper is to describe the design objectives, fundamental components and implementation of our real-time, EMG-driven virtual arm.

How muscle architecture and moment arms affect wrist flexion-extension moments

The purpose of this investigation was to determine how the moment arms and architecture of the wrist muscles influence their isometric moment-generating characteristics. A three-dimensional computer graphic model was developed that estimates the moment arms, maximum isometric forces, and maximum isometric flexion-extension moments generated by 15 muscles about the wrist over a range of wrist flexion angles. In combination with measurements of muscle strength, we used this model to answer three questions: (1) why is peak wrist flexion moment greater than peak extension moment, (2) why does flexion moment vary more with wrist flexion angle than does extension moment, and (3) why does flexion moment peak with the wrist in a flexed position? Analysis of the model revealed that the peak flexion moment is greater than the peak extension moment primarily because of the larger (110%) summed physiologic cross-sectional area of the flexors. The larger variation of flexion moment with flexion angle is caused mainly by greater variation of the moment arms of the major wrist flexors with flexion angle. The location of the peak flexion moment is determined by the wrist flexion moment arms (which tend to increase with wrist flexion) in combination with the force-length characteristics of these muscles.

Muscle activity in rapid multi-degree-of-freedom elbow movements: solutions from a musculoskeletal model.

Abstract. The activity of certain muscles that cross the elbow joint complex (EJC) are affected by forearm position and forearm movement during elbow flexion/extension. To investigate whether these changes are based on the musculoskeletal geometry of the joint, a three-dimensional musculotendinoskeletal computer model of the EJC was used to estimate individual muscle activity in multi-degree-of-freedom (df) rapid (ballistic) elbow movements. It is hypothesized that this model could reproduce the major features of elbow muscle activity during multi-df elbow movements using dynamic optimal control theory, given a minimum-time performance criterion. Results from the model are presented and verified with experimental kinematic and electromyographic data from movements that involved both one-df elbow flexion/extension and two-df flexion/extension with forearm pronation/supination. The model demonstrated how the activity of particular muscles is affected by both forearm position and movement, as measured in these experiments and as previously reported by others. These changes were most evident in the flexor muscles and least evident in the extensor muscles. The model also indicated that, for specific one- and two-df movements, activating a muscle that is antagonistic or noncontributory to the movement could reduce the movement time. The major features of muscle activity in multi-df elbow movements appear to be highly dependent on the joints musculoskeletal geometry and are not strictly based on neural influences or neuroanatomical substrates.

Individual muscle force parameters and fiber operating ranges for elbow flexion-extension and forearm pronation-supination.

We have quantified individual muscle force and moment contributions to net joint moments and estimated the operating ranges of the individual muscle fibers over the full range of motion for elbow flexion/extension and forearm pronation/supination. A three dimensional computer graphics model was developed in order to estimate individual muscle contributions in each degree of freedom over the full range of motion generated by 17 muscles crossing the elbow and forearm. Optimal fiber length, tendon slack length, and muscle specific tension values were adjusted within the literature range from cadaver studies such that the net isometric joint moments of the model approximated experimental joint moments within one standard deviation. Analysis of the model revealed that the muscles operate on varying portions of the ascending limb, plateau region, and descending limb of the force-length curve. This model can be used to further understand isometric force and moment contributions of individual muscles to net joint moments of the arm and forearm and can serve as a comprehensive reference for the forces and moments generated by 17 major muscles crossing the elbow and wrist.

Real-time haptic-teleoperated robotic system for motor control analysis

A versatile teleoperated robotic system was created as an assessment device for testing upper-extremity motor control adaptation using different control strategies. While many systems display output virtually on a computer monitor, this system was designed to output in three-dimensional physical space. The system accepts haptic force and torque input, and outputs robot end-effector displacements and rotations in three spatial dimensions. Benefits of this system include flexibility to conduct a variety of dissimilar tasks and reality of user feedback in physical space. Two separate experiments validated the teleoperated robotic system. The first experiment tested unimanual human motor control and the second tested bimanual motor control. This teleoperated robotic system can be used as an assessment device to study neuromuscular adaptability via a variety of control strategies providing a new and functional approach to human motor control analysis.

A Neural Network Approach to Electromyographic Signal Processing for a Motor Control Task

A hybrid modeling structure composed of a one degree of freedom computational musculoskeletal model and a feedforward multi-layer perceptron neural network was used to effectively map electromyography (EMG) from a human exercise trial to muscle activations in a physiologically feasible and accurate fashion. Several configurations of the complete hybrid system were used to map four muscle surface EMGs from a ballistic elbow flexion to normalized muscle activations, estimated individual muscle forces and torque about the joint. The net joint torque was used to train the neural portion of the hybrid system to minimize kinematic error. The model allowed the estimation of the nonobservable parameters: normalized muscle activations and forces which was used to penalize the learning system. With these parameters in the learning equation, our system produced muscle activations consistent with the classic triphasic response present in ballistic movements.

Genetically-designed Neural Networks for Error Reduction in an Optimized Biomechanical Model of the Human Elbow Joint Complex

A real time dynamic biomechanical model of the human elbow joint has been used as the first step in the process of calculating time varying joint position from the electromyograms (EMGs) of eight muscles crossing the joint. Since calculation of position has a high sensitivity to errors in the model torque calculation, a genetic algorithm (GA) neural network (NN) has been developed for automatic error reduction in the dynamic model. Genetic algorithms are used to design many neural network structures during a preliminary trial effort, and then each networks performance is ranked to choose a trained network that represents the most accurate result. Experimental results from three subjects have shown model error reduction in 84.2% of the data sets from a subject on which the model had been trained, and 52.6% of the data sets from the subjects on which the model had not been trained. Furthermore, the GA networks reduced the error standard deviation across all subjects, showing that progress in error reduction was made evenly across all data sets.

A kinematic analysis on an ACL deficient knee

A torn anterior cruciate ligament (ACL) is one of the more frequently occurring knee injuries plaguing both athletes and the general population [1]. This injury typically results in severe knee instability thereby limiting the activities the injured is able to perform. Currently, surgical reconstruction is the most common option to restore knee stability and allow the injured subject to return to full functionality (i.e. participation in athletic and recreational activities as desired). However, small populations of individuals who rupture their ACL forego surgery yet still remain fully functional [2]. We hypothesize that these subjects, referred to as “copers”, alter the control strategy of the muscles crossing the knee joint to compensate for their ACL-deficient knee.Copyright

Using musculoskeletal properties to develop a "normalized potential moment contribution index" for individual arm muscles

Musculoskeletal models serve as research tools to study the effect of individual muscle forces across multiple joints. Models are already used as diagnostic tools for treatment and rehabilitation such as in tendon transfer surgeries [1,2].Copyright

A neural network approach to electromyographic signal processing for a motor control task

The authors propose a novel signal processing technique employing both neural networks and classical signal processing methods to effectively map the surface electrical signal concomitant with muscle contraction (EMG) to human muscle activation. With a computational musculoskeletal model it is shown that these predicted muscle activations, accurately estimate joint torque for various ballistic flexion exercises. Through the systems ability to generalize across exercise trials and predict a classic ballistic triphasic activation pattern, a hybrid musculoskeletal system may be able to accurately and reliably model extremely complex physiological systems with clinical implications.