A. de Paor

A Monocular Marker-Free Gait Measurement System

This paper presents a new, user-friendly, portable motion capture and gait analysis system for capturing and analyzing human gait, designed as a telemedicine tool to monitor remotely the progress of patients through treatment. The system requires minimal user input and simple single-camera filming (which can be acquired from a basic webcam) making it very accessible to nontechnical, nonclinical personnel. This system can allow gait studies to acquire a much larger data set and allow trained gait analysts to focus their skills on the interpretation phase of gait analysis. The design uses a novel motion capture method derived from spatiotemporal segmentation and model-based tracking. Testing is performed on four monocular, sagittal-view, sample gait videos. Results of modeling, tracking, and analysis stages are presented with standard gait graphs and parameters compared to manually acquired data.

A stochastic limit cycle oscillator model of the EEG

We present an empirical model of the electroencephalogram (EEG) signal based on the construction of a stochastic limit cycle oscillator using Itô calculus. This formulation, where the noise influences actually interact with the dynamics, is substantially different from the usual definition of measurement noise. Analysis of model data is compared with actual EEG data using both traditional methods and modern techniques from nonlinear time series analysis. The model demonstrates visually displayed patterns and statistics that are similar to actual EEG data. In addition, the nonlinear mechanisms underlying the dynamics of the model do not manifest themselves in nonlinear time series analysis, paralleling the situation with real, non-pathological EEG data. This modeling exercise suggests that the EEG is optimally described by stochastic limit cycle behavior.

A simple soft limiter describing function for biomedical applications

This paper suggests an arctangent function as a suitable parameterisation for the soft-limiting gain characteristic frequently encountered in models of biomedical systems. This function is shown, as an example, to fit the neural arc component of the baroreflex with the main contribution of the paper being the development of a simple describing function (DF) characteristic for the arctangent. The simple form of the DF allows transparency of the physiological parameters in, for example, stability analysis. For illustration, the derived DF is used to examine low-frequency limit cycles in blood pressure, sometimes termed Mayer waves.

Correction to the Classical Two-Species Hall Coefficient Using Twoport Network Theory

By the use of twoport network theory, the classical expression for the two-species Hall Coefficient, R h = (pμ 2 p -nμ 2 n ) q(pμ p +pμ n ) 2 is, on admission of terms neglected by earlier authors, corrected to R n = {pμ 2 p -nμ 2 n +B 2 r μ 2 p μ 2 n (p-n)} The new expression approximates the old when |B γ | <<min {1/μ m 1/μ p }. The condition of approximation establishes the range of field strength in which the classical expression is acceptable. Since the Hall Effect is part of the stock-in-trade of electrical engineers, however, it seems important that students should be aware of the correct expression.

Phoneme Recognition Based Software System for Computer Interaction by Disabled People

This paper describes a software-based system for communication and control by disabled people based on automatic recognition of phonemes. This program allows users to navigate around an alphabet board by making phonemic utterances, thus enabling the user to spell out messages. Phoneme recognition provides an alternative to speech recognition technologies for people who have lost the ability to speak (often due to a stroke, accident or other disabling condition) but remain capable of producing simple repeatable utterances

A model for electrically activated mammalian muscle capturing N-let behaviours

A mathematical model for electrically activated mammalian muscle is outlined. The presented model is developed by considering the underlying biophysics of the muscle contraction mechanism. This model accounts for the nonlinear effects seen in the use of high frequency stimulation patterns, specifically doublets and N-lets. Such stimulation patterns are used to improve fatigue resistance, and maximise the force-time integral per pulse of electrically stimulated muscles. A mechanism for N-let effects based upon known biophysical phenomena is implemented. Experimental and simulation results are presented and compared. This model may be useful in the design of control strategies for muscles using N-lets.

Nonstationary stochastic resonance in a reduced-order Hodgkin-Huxley neuron

A physiologically realistic neural system model is shown to be able to detect a weak nonstationary signal through the addition of noise. It is shown that the signal transduction performance is optimised for a nonzero value of noise intensity in a manner suggestive of stochastic resonance.

An oscillatory neural network scheme for temporal encoding and stimulus recognition

A novel computational neuro-architecture based on the phase resetting properties of physiologically based neural oscillators is proposed, analog input variables are encoded in the patterns of the firing times with individual recognition units operating as radial basis-functions.