Biomedical subjects

Thomas S Buchanan

Publications and source records attributed to Thomas S Buchanan.

22 records · Page 2Linked to original sources

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↗

PubMed2002

Force transmission through the juvenile idiopathic arthritic wrist: a novel approach using a sliding rigid body spring model.

Force transmission across the wrist during a grasping maneuver of the hand was simulated for three children with juvenile idiopathic arthritis (JIA) and for one healthy age-matched child. Joint reaction forces were estimated using a series of springs between articulating bones. This method (i.e., rigid body spring modeling) has proven useful for examining loading profiles for normally aligned wrists. A novel method (i.e., sliding rigid body spring modeling) designed specifically for studying joint reaction forces of the malaligned JIA wrist is presented in this paper. Loading profiles across the wrist for the unimpaired child were similar using both spring modeling methods. However, the traditional fixed-end method failed to converge to a solution for one of the JIA subjects indicating the sliding model may be more suitable for investigating loading profiles of the malaligned wrist. The results of this study suggest that a larger proportion of force is transferred through the ulno-carpal joint of the JIA wrist than for healthy subjects, with a less than normal proportion of force transferred through the radio-carpal joint. In addition, the ulnar directed forces along the shear axis defined in this study were greater for all three JIA children compared to values for the healthy child. These observations are what were hypothesized for an individual with JIA of the wrist.

Arthritis↗

PubMed2002

Scaling of peak moment arms of elbow muscles with upper extremity bone dimensions.

It is often assumed that moment arms scale with size and can be normalized by body segment lengths or limb circumferences. However, quantitative scaling relationships between moment arms and anthropometric dimensions are generally not available. We hypothesized that peak moment arms of the elbow flexor and extensor muscles scale with the shorter distance (D(s)) between the elbow flexion axis and a muscle's origin and insertion. To test this hypothesis, we estimated moment arms of six muscles that cross the elbow, digitized muscle attachment sites and bone surface geometry, and estimated the location of the elbow flexion axis in 10 upper extremity cadaveric specimens which ranged in size from a 5'0" female to a 6'4" male. D(s) accurately reflected the differences in peak moment arms across different muscles, explaining 93-99% of the variation in peaks between muscles in the same specimen. D(s) also explained between 55% and 88% of the interspecimen variation in peak moment arms for brachioradialis, biceps, and ECRL. Triceps peak moment arm was significantly correlated to the anterior-posterior dimension of the ulna measured at the olecranon (r(2)=0.61, p=0.008). Radius length provides a good measure of the interspecimen variation in peaks for brachioradialis, biceps, and ECRL. However, bone lengths were not significantly correlated to triceps moment arm or anterior-posterior bone dimensions. This work advances our understanding of the variability and scaling dimensions for elbow muscle moment arms across subjects of different sizes.

Arm↗

PubMed2002

Prediction of joint moments using a neural network model of muscle activations from EMG signals.

Because the relationship between electromyographic (EMG) signals and muscle activations remains unpredictable, a new way to determine muscle activations from EMG signals by using a neural network is proposed and realized. Using a neural network to predict the muscle activations from EMG signals avoids establishing a complex mathematical model to express the muscle activation dynamics. The feed-forward neural network model of muscle activations applied here is composed of four layers and uses an adjusted back-propagation training algorithm. In this study, the basic back-propagation algorithm was not applicable, because muscle activation could not be measured, and hence the error between predicted activation and the real activation was not available. Thus, an adjusted back-propagation algorithm was developed. Joint torque at the elbow was calculated from the EMG signals of ten flexor and extensor muscles, using the neural network result of estimated activation of the muscles. Once muscle activations were obtained, Hill-type models were used to estimate muscle force. A musculoskeletal geometry model was then used to obtain moment arms, from which joint moments were determined and compared with measured values. The results show that this neural network model can be used to represent the relationship between EMG signals and joint moments well.

Action Potentials↗