William W. Armstrong

The dynamics of articulated rigid bodies for purposes of animation

Curves and surfaces satisfying continuity and smoothness conditions are used in computer graphics to fit spatial data points. In a similar fashion, smooth motions of objects should be available to animators in such a way that the dynamics are correct to the degree required for realism. The motion, like a curve or surface shape, should be controllable by easy manipulations of a set of control parameters or by real-time interaction between the animator and a scene generated by dynamic simulation. In this paper, the objects considered have the form of rigid links joined at hinges to form a tree. This is a reasonable first approximation to human and animal bodies. The equations of motion are formulated with respect to hinge-centered coordinates, and are solved by an efficient technique in time which grows linearly with the number of links.

Sensory nerve recording for closed-loop control to restore motor functions

A method is developed for using neural recordings to control functional electrical stimulation (FES) to nerves and muscles. Experiments were done in chronic cats with a goal of designing a rule-based controller to generate rhythmic movements of the ankle joint during treadmill locomotion. Neural signals from the tibial and superficial peroneal nerves were recorded with cuff electrodes and processed simultaneously with muscular signals from ankle flexors and extensors in the cats hind limb. Cuff electrodes are an effective method for long-term chronic recording in peripheral nerves without causing discomfort or damage to the nerve. For real-time operation the authors designed a low-noise amplifier with a blanking circuit to minimize stimulation artifacts. They used threshold detection to design a simple rule-based control and compared its output to the pattern determined using adaptive neural networks. Both the threshold detection and adaptive networks are robust enough to accommodate the variability in neural recordings. The adaptive logic network used for this study is effective in mapping transfer functions and therefore applicable for determination of gait invariants to be used for closed loop control in an FES system. Simple rule-bases will probably be chosen for initial applications to human patients. However, more complex FES applications require more complex rule-bases and better mapping of continuous neural recordings and muscular activity. Adaptive neural networks have promise for these more complex applications.<<ETX>>

Machine learning in control of functional electrical stimulation systems for locomotion

Two machine learning techniques were evaluated for automatic design of a rule-based control of functional electrical stimulation (FES) for locomotion of spinal cord injured humans. The task was to learn the invariant characteristics of the relationship between sensory information and the FES-control signal by using off-line supervised training. Sensory signals were recorded using pressure sensors installed in the insoles of a subjects shoes and goniometers attached across the joints of the affected leg. The FES-control consisted of pulses corresponding to time intervals when the subject pressed on the manual push-button to deliver the stimulation during FES-assisted ambulation. The machine learning techniques used were the adaptive logic network (ALN) and the inductive learning algorithm (IL). Results to date suggest that, given the same training data, the IL learned faster than the ALN while both performed the test rapidly. The generalization was estimated by measuring the test errors and it was better with an ALN, especially if past points were used to reflect the time dimension. Both techniques were able to predict future stimulation events. An advantage of the ALN over the IL was that ALNs can be retrained with new data without losing previously collected knowledge. The advantages of the IL over the ALN were that the IL produces small, explicit, comprehensible trees and that the relative importance of each sensory contribution can be quantified.<<ETX>>

Near-Real-Time Control of Human Figure Models

Animating human figures is one of the major problems in computer animation. A recent approach is the use of dynamic analysis to compute the movement of a human figure, given the forces and torques operating within and upon the body. One of the problems with this technique is computing the forces and torques required for particular motions; this has been called the control problem of dynamic analysis. To develop a better understanding of this problem, an interactive interface to a dynamics package has been produced. This interface, along with a collection of low-level motion processes, can be used to control the motion of a human figure model. This article describes both the user interface and the motion processes, along with our experiences with this approach.

Adaptation Algorithms for Binary Tree Networks

The automatic synthesis of Boolean switching functions by adaptive tree networks is discussed. The concept of heuristic responsibility, by means of which parts of a tree become specialized to certain subsets of input vectors, is explained. Applications to pattern recognition and optical character recognition (OCR) problems are described.

Dynamics for animation of characters with deformable surfaces

The animation of human and human-like characters is one of the major problems in computer animation (Badler 1982). The key aspect of this problem is achieving realistic motion with a minimal amount of effort on the part of the animator. The control of a dynamic animation by means of joint torque and force functions has been described by Wilhelms and Barsky (1985). Their solution to the dynamics problem uses a Gibbs-Appell formulation, according to which the time to set up the equations grows at least as the cube of the number of links. After that, the solution time is at least quadratic, due to the fact that a square matrix having several rows for each link is employed. Formulating the problem using such a matrix is not essential for animating tree-like figures, however. The fact that the only dynamic interactions which occur between links are those between parents and their children means that the solution can be carried out more efficiently.

Improved Methods for Control of FES for Locomotion

Abstract Two methods of generating the rules for control systems for control of functional electrical stimulation (FES) in locomotion of subjects with incomplete spinal cord injury were studied and qualitatively compared. The aim of the rule-based control system was to synthesize and replace the manual FES-switching function operated by the subject or a skilled physiotherapist. The first method, “hand-crafting” of rules, requires very detailed signal analysis by an experienced person, which may result in systems that are limited either by their functionality or safety for the subject. The second method, i.e. automatic generation of rules through learning from examples by a machine learning program, does not have such limitations because it is capable of learning everything that is presented to it during the training phase. It is obvious that for simple control systems, “hand-crafted” rules are a fast and simple solution, but for complex multichannel FES applications automatic generation of the rules can save many hours of trial-and-error experiments with hand-crafted rules.

Learning of EMG-patterns by adaptive logic networks

An adaptive logic network (ALN) has been used for nonparametric identification of the system consisting of two peripheral nerves and two muscles in the freely moving cat. The aim of this identification was to design a rule-based control for a functional electrical stimulation (FES) of the cats hind limb. We recorded from peripheral nerves and muscles while a chronic cat walked an a powered treadmill. We noticed a very reproducible firing in tibial and superficial peroneal nerves related to patterns of EMG activity in medial gastrocnemius (MC) and anterior tibialis (AT) muscles in the cats hind limb with respect to a phase of the gait cycle (e.g. beginning stance, beginning swing). We succeeded to restore and predict EMG activity of MG and AT muscles from neural recordings after applying Adaptive Logic Networks (ALNs). ALN is a type of artificial neural network having a tree configuration and using a Boolean operations in its nodes. We supplied a training set consisting of coded recordings from nerves and muscles (20 cats steps were sampled at 50 Hz) for supervised training. Applied encoding and training parameters resulted in a reasonably short training time and high correlation between an originai and predicted EMG signals. The variance accounted for (VAF) by the ALN prediction of the test data was 80% qualify- ing this technique as a good candidate for implementation in real-time FES control systems.

Evaluation of Adaptive Logic Networks for control of walking in paralyzed patients

An Adaptive Logic Network (ALN), a type of Neural Network (NN), as evaluated for the control of walking in Spinal Cord Injured (SCI patients. The motivation behind this research was to explore more reliable methods for control of simple Functional Neuromuscular Stimulation (FNS) systems in incomplete SCI patients. The ALN was used to recognize a patients intention to make a step by stimulating muscles in a partially paralyzed leg. Signals from four force sensors, installed under the toes and heels, have been used as inputs and a gating pulse associated with the stimulation as an output for learning and testing the function of the control system. Manual control, by either the patient or a physiotherapist, has been used as a template to be matched by the ALN. Generalization of the learned functions by the· ALN to previously unseen data was also tested. Finally, we manipulated the number of input channels, the inclusion of information from past samples and prediction of future events. The ALN is capable of generating the same time series of output pulses as those generated by “human experts.” Furthermore, it can predict the stimulation event early enough so that the requirement for stimulation can be verified and the patient informed to prepare for stimulation.

Adaptive Logic Networks for Facial Feature Detection

The task of automatic facial feature detection in frontal-view, ID-type pictures is considered. Attention is focused on the problem of eye detection. A neural network approach is tested using adaptive logic networks, which are suitable for this problem on account of their high evaluation speed on serial hardware compared to that of more common multilayer perceptrons. We present theoretical reasoning and experimental results. The experiments are carried out with images of different clarity, scale, lighting, orientation and backgrounds.