Biomedical subjects

Roger V Gonzalez

Publications and source records attributed to Roger V Gonzalez.

3 recordsLinked to original sources

PubMed2005

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.

Arm↗

PubMed2004

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 network's 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.

Algorithms↗

PubMed2002

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 muscle's 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.

Arm↗