John D. Enderle

Time-Optimal Control of Saccadic Eye Movements

A new theory describing the time-optimal control of saccadic eye movements is proposed based on Pontryagins minimum principle and physiological considerations. The lateral and medial rectus muscle of each eye is assumed to be a parallel combination of an active state tension generator with a viscosity and elastic element, connected to a series elastic element. The eyeball is modeled as a sphere connected to a viscosity and elastic element. Each of these elements is assumed to be ideal and linear. The neuronal control strategy is shown to be a first-order time-optimal control signal. Under this condition, the active state tension for each muscle is a low-pass filtered pulse-step waveform. The magnitude of the agonist pulse is a maximum for saccades of all sizes and only the duration of the agonist pulse affects the size of the saccade. The antagonist muscle is completely inhibited during the period of maximum stimulation for the agonist muscle. Horizontal saccadic eye movements were recorded from infrared signals reflected from the anterior surface of the cornea and then digitized. Parameter estimates for the model were calculated by using a conjugate gradient search program which minimizes the integral of the absolute value of the squared error between the model and the data. The predictions of the model under a time-optimal controller are in good agreement with the data.

Neural control of saccades.

Quantitative models of the oculomotor plant and control of the saccadic eye movement system are presented in this chapter. Oculomotor plant models described here are linear, including a second-order model by Westheimer (1954), Bahill et al. (1980) and Enderle et al. (2000). The model of the saccade generator is initiated by the superior colliculus and terminated by the cerebellar fastigial nucleus that operates under a time optimal control strategy. A common mechanism for all types of saccades is described, including those with dynamic overshoot and glissadic behavior. Conflicting evidence exists regarding the operation of the excitatory burst neuron during saccades. The excitatory burst neuron operates within two states: complete inhibition, and without inhibition that is characterized by high firing at rates of up to 1000 Hz. While there is direct evidence of projections from the superior colliculus to the paramedian pontine reticular formation, there is conflictory evidence regarding the connections from the superior colliculus to the excitatory burst neuron, with the most recent experimental results supporting no direct connections. A model of the excitatory burst neuron is described using a Hodgkin-Huxley model of the neuron that fires at 1000 Hz automatically and without stimulation when released from inhibition. SIMULINK simulations using this neuron model have all of the characteristics of the excitatory burst neuron firing rate during a saccade. This model eliminates the need to introduce BIAS inputs that causes bursting in some models of the saccade generator. Such a model is also appropriate for modeling the Omnipause neurons.

Passive RFID Asset Monitoring System in Hospital Environments

Passive Radio-Frequency Identification (RFID) technique is implemented in Hartford Hospital, Connecticut, for asset tracking. In particular, the technique is employed to monitor Telemetry Transmitters (TT) in order to prevent losing them. The work exploits the capabilities of passive RFID technology to determine the location of TTs as they pass through certain key spots in the hospital. The asset monitoring system is able to communicate with the hospital equipment management database, in order to get queries or send other necessary commands. An alarm feature is designed to alert the responsible staff members when one of the TTs passes through the common identifiable spots. Based on the proposed asset monitoring prototype, a Return on Investment (ROI) analysis and evaluation is conducted to justify the feasibility of installing a hospital-wide asset monitoring system with passive RFID solution.

Brain-Machine Interface Engineering

Neural interfaces are one of the most exciting emerging technologies to impact bioengineering and neuroscience because they enable an alternate communication channel linking directly the nervous system with man-made devices. This book reveals the essential engineering principles and signal processing tools for deriving control commands from bioelectric signals in large ensembles of neurons. The topics featured include analysis techniques for determining neural representation, modeling in motor systems, computing with neural spikes, and hardware implementation of neural interfaces. Beginning with an exploration of the historical developments that have led to the decoding of information from neural interfaces, this book compares the theory and performance of new neural engineering approaches for BMIs.

A comparison of static and dynamic characteristics between rectus eye muscle and linear muscle model predictions

The muscle is modeled as a viscoelastic parallel combination connected to a parallel combination of active state tension generator, viscosity element, and length tension elastic element. Each of the elements is linear and their existence is supported with physiological evidence. The static and dynamic properties of the muscle model are compared to rectus eye muscle data. The length-tension characteristics of the model are in good agreement with the data within the operating region of the muscle. With the muscle model incorporated into a lever system to match the isotonic experiment paradigm, simulation results for this linear system yield a nonlinear force-velocity curve. Moreover, the family of force-velocity curves generated with different stimulus rates reported in the literature match the predictions of the model without parametric changes.<<ETX>>

Ensuring that biomedical engineers are ready for the real world

Discusses preparing biomedical engineering students for real-world problem solving by putting theory into practice in the curriculum. It is concluded that mechanisms for preparing biomedical engineering students for real-world problem solving are numerous. Failure to incorporate such real-world experiences throughout the curriculum creates frustration for the student, particularly for the freshman or sophomore undergraduate who lacks the experience to draw a connection between theory and practice. Upon graduation, the biomedical engineer is suddenly confronted with real-world problems and design challenges that require a team of experts, project planning and execution, regulatory and quality control, financial support, and a satisfied customer. Too often, graduates are unprepared for this transition to real-world engineering.

Frequency response analysis of human saccadic eye movements: Estimation of stochastic muscle forces☆

A frequency response method is used to estimate parameters of a fourth-order model of the oculomotor system and the active state tensions during a saccadic eye movement. The lateral and medial rectus muscle of each eye is modeled as a parallel combination of an active state tension generator with a viscosity and elastic element, connected to a series elastic element. The eyeball is modeled as a sphere connected to a viscosity and elastic element. Each of these elements is assumed to be ideal and linear. The active state tension for each muscle is modeled by a low-pass filtered pulse-step waveform. Initial estimates of the oculomotor mechanical components are based on physiological evidence. Initial estimates of the active state tension are based on an extrapolation of the eye movement trajectory. Horizontal saccadic eye movements were recorded from infrared signals reflected from the anterior surface of the cornea and then digitized. Parameter estimates were calculated for the model by using a conjugate gradient search program which minimizes the integral of the absolute value of the squared error between the model and the data. The predictions of the model are shown to be in good agreement with the data. Final estimates of motoneuronal activity demonstrate that the agonist muscle is maximally stimulated during the early portion of a saccadic eye movement regardless of the amplitude of the saccade; only the duration of the maximal stimulation affects the size of the saccade. The antagonist muscle is completely inhibited during the period of maximum agonist muscle stimulation. Furthermore, it is demonstrated that saccade motoneuronal activity is a stochastic phenomenon.

AN UPDATED TIME-OPTIMAL 3RD-ORDER LINEAR SACCADIC EYE PLANT MODEL

A linear third-order model of the ocular motor plant for horizontal saccadic eye movements is presented consisting of a linear ocular motor plant and a time-optimal saccadic controller based on physiological considerations. The ocular motor plant consists of the eyeball and two extraocular muscles. All parameters and initial conditions are estimated or measured from physiological data. The neural inputs are described by pulse-slide-step waveforms with a post inhibitory rebound burst and based on a time-optimal controller. Model parameters are estimated using the system identification technique. The static and dynamic behaviors of the model are in excellent agreement with the experimental data.

The Linear Homeomorphic Saccadic Eye Movement Model - A Modification

The objective of this study was the modification of a linear homeomorphic horizontal saccadic eye movement model to a direct programming state-space representation through Laplace variable analysis about the operating point or initial eye position. The lateral and medial rectus muscle of each eye is modeled as a parallel combination of an active state tension generator with a viscosity and elastic element, connected to a series elastic element. The eyeball is modeled as a sphere connected to a viscosity and elastic element. Each of these elements is assumed to be ideal and linear.

Models of Horizontal Eye Movements, Part II:A 3rd Order Linear Saccade Model

There are five different types of eye movements: saccades, smooth pursuit, vestibular ocular eye movements, optokinetic eye movements, and vergence eye movements. The purpose of this book is focused primarily on mathematical models of the horizontal saccadic eye movement system and the smooth pursuit system, rather than on how visual information is processed. A saccade is a fast eye movement used to acquire a target by placing the image of the target on the fovea. Smooth pursuit is a slow eye movement used to track a target as it moves by keeping the target on the fovea. The vestibular ocular movement is used to keep the eyes on a target during brief head movements. The optokinetic eye movement is a combination of saccadic and slow eye movements that keeps a full-field image stable on the retina during sustained head rotation. Each of these movements is a conjugate eye movement, that is, movements of both eyes together driven by a common neural source. A vergence movement is a non-con ugate eye movement allowing the eyes to track targets as they come closer or farther away. In this book, a 2009 version of a state-of-the-art model is presented for horizontal saccades that is 3rd-order and linear, and controlled by a physiologically based time-optimal neural network. The oculomotor plant and saccade generator are the basic elements of the saccadic system. The control of saccades is initiated by the superior colliculus and terminated by the cerebellar fastigial nucleus, and involves a complex neural circuit in the mid brain. This book is the second part of a book series on models of horizontal eye movements. Table of Contents: 2009 Linear Homeomorphic Saccadic Eye Movement Model and Post-Saccade Behavior: Dynamic and Glissadic Overshoot / Neural Network for the Saccade Controller